Privacy and security enhancement in smart cities using advanced cryptographic techniques.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Apr 1;16:17262. doi: 10.1038/s41598-026-46364-6 Privacy and security enhancement in smart cities using advanced cryptographic techniques Kirti Dinkar More 1 , Dhanya Pramod 2, ✉ Author information Article notes Copyright and License information 1 Symbiosis Centre for Research and Innovation (SCRI), Symbiosis International (Deemed University), Pune, India 2 Symbiosis Centre for Information Technology, Symbiosis International (Deemed University), Pune, India ✉ Corresponding author. Received 2025 Dec 1; Accepted 2026 Mar 25; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13233845  PMID: 41922449 Abstract Smart city technologies are advancing rapidly, creating previously unattainable opportunities for improved services, quality of life, and effective urban management. These improvements, however, can present serious privacy and security threats to sensitive data. The proposed study presents a comparative performance evaluation model - FHE and ABE with Fast Exponentiation Optimization (FA-FEO). To address data security challenges in smart city environment monitoring systems, the proposed study examines the performance of advanced cryptographic techniques, such as attribute-based and homomorphic encryption, in the context of smart cities. An optimization strategy is applied to improve performance. By leveraging Attribute-Based Encryption (ABE) for access control and Fully Homomorphic Encryption (FHE) for computations on encrypted data, these cryptographic methods guarantee data confidentiality and flexible access management. Our system evaluates how well FHE and two variants of ABE, Key-Policy Attribute-Based Encryption (KP-ABE) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), protect environmental information collected from Internet of Things (IoT) devices, including air quality data. Experimental work is conducted using network simulation to evaluate performance metrics, including key generation and execution times. An optimized key generation procedure increases system efficiency, as demonstrated by performance evaluations. Experimental results demonstrate notable computational gains. Across all schemes (FHE, KP-ABE, CP-ABE), the optimization strategy - fast exponentiation considerably improved efficiency, reducing key generation time from approximately 20–22 s to less than 1s. This corresponds to approximately a 96–97% reduction for KP-ABE, CP-ABE, and FHE compared with the naive approach. Our previous study has already reported other desirable performance evaluation metrics, including PDR, Consumed energy, power consumption, latency, execution time, normalized overhead, speed, throughput, and memory required. Keywords: Cryptography, Smart city, FHE, KP-ABE, CP-ABE Subject terms: Engineering, Mathematics and computing Introduction To enhance urban living, smart cities employ intelligent systems, networked devices, and advanced analytics. The development of a smart city requires integrating multiple technologies while overcoming challenges across data systems, communication protocols, and the Internet of Things. To achieve flexibility and scalability, the architecture must make key choices about data processing, performance, security, confidentiality, and communication. The IoT architecture comprises multiple layers. Mainly, the device layer, which comprises sensors that collect data; the network layer, which ensures secure data transmission; and the application layer, which handles IoT services like smart cities and homes. Additional layers include the session and business layers. The session layer manages IoT connections using protocols such as the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP). The business layer defines procedures, analyzes data, and develops strategies. Regular monitoring, evaluations, and incident response plans are necessary to address changing security challenges. Interoperability, which ensures seamless data interchange and communication between diverse systems and devices, is a crucial element of IoT systems. This is addressed by the IoT reference architecture, which uses standardized protocols such as Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and Hypertext Transfer Protocol (HTTP) to ensure compatibility across manufacturers and technologies. It also employs a layered system design, where different duties are managed by each layer, which facilitates the integration and management of system components 1 . An integral component of the ecosystem of smart cities is environmental monitoring, which gathers and transmits data on environmental factors related to air quality via IoT sensors. Although this data is essential for making informed decisions, it is also susceptible and prone to security breaches that might compromise individual trust and privacy 2 . Conventional encryption techniques provide a basic level of security, but they are insufficient for dynamic smart city applications that require real-time analysis and data sharing. Environmental monitoring systems, for instance, need to interpret data from multiple sensors, communicate insights to different stakeholders, and preserve the privacy of the original data. Therefore, it has become essential to use strong advanced encryption techniques 3 . To address these demands, ABE and FHE have emerged as potentially effective strategies. Throughout processing, FHE maintains confidentiality by enabling calculations on encrypted data without decryption 4 , 5 . Secure and adaptable data exchange is enabled by ABE’s granular access control, which grants decryption permissions based on matching user attributes 6 . The practical application of FHE and ABE in smart city scenarios, especially for environmental monitoring, remains challenging despite their potential benefits. Finding a balance between strong security and computational efficiency, maintaining scalability for large-scale deployments, and incorporating new cryptographic models into existing IoT architectures are among the main concerns. In the proposed system, comparative results of FHE, KP-ABE, and CP-ABE are analyzed for their suitability in the IoT environment. Various performance metrics, such as key generation timings, consumed energy, power consumption, PDR (Packet Delivery Ratio), latency, execution time, throughput, overheads, jitter, number of packets dropped, memory required, and resource utilization, are considered in performance evaluation 7 . An optimized key generation method is incorporated. This results in increased system performance, as shown in the performance analysis, making the system suitable for maintaining security in an IoT context, such as a smart city. Experimental work demonstrates the system’s practical potential and encourages confidence in its future hardware deployment. We organize the rest of the paper as follows: Sect.  2 reviews related work. Section  3 presents the proposed system and outlines our security model. Section  4 presents results and discussion covering scalability considerations and deployment challenges, threat model, and adversarial assumptions, and Sect.  5 concludes the paper. Related work According to researchers, ABE is increasingly being used in the IoT context to improve access control and data protection. An investigation was conducted by authors into the feasibility of ABE on low-resource IoT platforms, including the Raspberry Pi Model B, Raspberry Pi Zero, Intel Edison, and Intel Galileo Gen 2. Even with the device’s limited processing capacity, ABE can effectively provide fine-grained access control without noticeably reducing performance. Use of ABE promotes safe data sharing and management for a variety of applications 8 . The article 9 presents an approach to enhance urban living conditions. It includes the use of sensors in an IoT network to collect environmental data, such as temperature, humidity, air quality, and to offer real-time monitoring. When using cryptography for secure communication and data privacy, system performance must be considered. The Session-Critical Distributed Authentication Mechanism (SCDAM) was introduced by the authors of[ 10 ] to enhance the data security in wireless IoT sensor networks. SCDAM employs an improved elliptic curve cryptography method for short-lived concealed authentication, taking into account session time and interruptions to ensure end-to-end security. This solution effectively eliminates insider attacks and reduces data loss by employing a linear hashing algorithm to ensure the non-repetition of session keys 10 . Article 11 evaluates the use of Attribute-Based Credentials (ABCs) to enhance privacy in Vehicular Ad hoc Networks (VANETs) for smart city road traffic services. They assess whether ABE systems are appropriate for a variety of use cases from the European Telecommunications Standards Institute’s fundamental set of applications. According to their experimental results, Idemix has emerged as the most promising solution for most cases, demonstrating the viability of ABE systems for VANETs 11 . To ensure safe and reliable communication in automotive networks, the Multi-Authenticate Homomorphic Signature Algorithm provides a strong solution for effectively authenticating multiple messages 12 . In smart cities, vital information about Autonomous Connected Vehicles (AVCs) is necessary for vehicle operation. Still, it also raises key privacy and security concerns, particularly regarding the possibility of cyberattacks, such as remote hacking and sensor data manipulation 13 . Paper 14 explores two encryption methods: CP-ABE and KP-ABE in the context of secure data retrieval for military networks. While KP-ABE links secret keys to user attributes to provide fine-grained control over access policies 14 , 15 , CP-ABE integrates access policies within ciphertext 15 , 16 . Both encryption methods offer strong security features, and their benefits differ according to the specific requirements of military data retrieval systems 14 . ABE ensures data confidentiality while providing fine-grained access control based on user attributes. Sensitive information, including personal data from smart city apps, is protected while still allowing authorized users to access it in compliance with established access policies. For large-scale smart city data-sharing systems, this study emphasizes the challenges of balancing efficiency, security, and privacy 17 . Analysis of encrypted information to ensure security is known as cryptanalysis. This technique enhances data security and privacy in IoT systems by permitting computations on encrypted data 18 . By integrating two S-boxes into its design, the proposed cryptosystem 19 maintains low computational overhead while offering robust security for a range of multimedia and real-time traffic across IoT networks. Maintaining this balance between security and efficiency is essential to the seamless operation of smart city infrastructure, where numerous interconnected devices must be protected from cyberattacks while handling data rapidly. The authors 20 explored the feasibility of employing homomorphic encryption techniques in Cyber-Physical Systems (CPSs). They demonstrate how data security and privacy can be enhanced by using these cryptographic techniques to perform calculations on encrypted data without decryption. To determine whether various homomorphic encryption schemes are appropriate for real-time CPS applications, this study considers factors such as resource consumption and computational efficiency. Although homomorphic encryption offers appealing opportunities for secure data processing in CPSs, implementation challenges and performance overhead must be addressed to enable widespread deployment. The authors in[ 21 ] evaluated the practicality of implementing FHE in devices with constrained resources, as those found in healthcare systems. They implemented privacy-preserving protocols on a PC (which simulated a cloud server) and a Raspberry Pi (which appeared to be a device with constrained resources) using two FHE libraries, HElib and SEAL. Their results show that the SEAL generated smaller ciphertexts, resulting in lower communication overhead and lower transmission energy consumption, making it more suitable for practical implementations in constrained contexts. HElib, on the other hand, offered quicker computation times but generated larger ciphertexts. Both libraries perform well on limited-resource devices. The FHE approach, which enables secure computation on encrypted healthcare data without the need for decryption, was proposed by magic number fragmentation and ElGamal encryption integration 22 . A privacy-preserving Attribute-Based Access Control (ABAC) framework for cyber-physical systems (CPS) is proposed in article 23 . It integrates Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP) to enable secure attribute evaluation without exposing sensitive data. This technique allows encrypted access control decisions to maintain confidentiality and policy integrity. It enhances privacy and trust in CPS environments by minimizing the revealing of data during authentication. However, high computational overhead, limited suitability for embedded devices and lack of real-world validation remain key challenges. The Privacy-Preserving Dual Reputation Updating Scheme with Multi-Dimensional Feedback Scores in Vehicle Platoon (PDRU) scheme 24 introduces a dual-reputation model combined with multi-dimensional feedback scoring to enhance robustness against single-reputation attacks in vehicle platoons. The scheme ensures privacy preservation of vehicle identities, feedback scores, and reputation values, while maintaining computational efficiency at the Trusted Authority (TA). Its strength lies in improving the reliability assessment of leading vehicles in coordinated platoon scenarios. Similarly, the Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks (PPRU) scheme 25 focuses on secure reputation updating in cloud-assisted vehicular networks. It preserves anonymity and minimizes communication and computation overhead at centralized authorities, making it suitable for dynamic vehicular infrastructures. Although PDRU and PPRU effectively address privacy-preserving trust management in vehicular networks, their design objectives differ significantly from the FA-FEO model proposed in this work. Specifically, PDRU and PPRU primarily address reputation updates and trust evaluation. In contrast, the proposed FA-FEO focuses on privacy-preserving computation over encrypted data and fine-grained access control in smart city IoT environments. The article 26 proposes a Traffic Security System that uses the BB84 protocol, Falcon signatures, and masking to protect smart city traffic data. It improves privacy and quantum resistance while maintaining low signing and verification times. However, it mainly targets traffic applications and may need adaptation for other IoT domains. Authors in[ 27 ] proposed a blockchain-based biometric multi-factor authentication protocol to enable secure data sharing and access control in drone-assisted battlefield IoT systems. The study 28 presents the Quantum-Enhanced Security for Smart Meters (QESM) framework, combining Kyber key exchange, Fast Fourier Transform over Lattice-based Cryptography (FALCON) signatures, and zero-knowledge proofs for secure smart grid communication. The system offers strong quantum resistance with low time and memory overhead. It focuses specifically on power grid systems. Recent work 29 has investigated quantum-safe blockchain-assisted encryption protocols to protect IoT networks against future quantum attacks, highlighting the need for secure, decentralized data protection in smart city systems. The gaps identified in existing work motivated our choice of ABE and FHE for the proposed model. Table 1 provides an overview of existing research on security methods and cryptographic techniques used in smart city infrastructures and IoT systems. Table 1. Review of security techniques for smart city and IoT systems. References Techniques / Algorithms / Cryptography method Used Research contribution Security / Environmental Relevance Challenges 30 Reviewed various techniques, including Hybrid Smart City Cyber Security Architecture (HSCCA), AI-based methods, Elliptic Curve Cryptography, Intrusion Detection and Prevention System (IDPS), IoT, CPSs, Wireless Sensor Network, and blockchain-based security approaches. Systematic review of 26 studies on smart city cyber security. Focuses on studies exploring the protection of critical infrastructure, data privacy, and public safety across core smart city services. Lack of unified security standards, scalability issues, and limited real-world validation of existing frameworks. 31 IOTA-based Distributed Ledger Technology (DLT), Fernet (Advanced Encryption Standard − 128 + Hash-based Message Authentication Code), and secure device authentication. Proposes a DLT-enabled secure architecture for smart buildings. Enhances secure, low-cost, and energy-efficient IoT communication in smart buildings. Performance degradation under congestion, scalability in large deployments, and the need for real-world validation. 32 Lightweight certificate less encryption, Schnorr signature, ECC, Decisional bilinear Diffie-Hellman (DBDH) -based security. Proposes a lightweight certificate-less encryption scheme resistant to key replacement and chosen-ciphertext attacks (CCA2) for smart city IoT. Ensures secure and efficient IoT communication in resource-constrained smart city environments. Scalability in large IoT deployments, dependency on pairing operations, and lack of real-world deployment evaluation. 33 Quantum-resistant hybrid encryption (QRHE-IoT), (Learning With Errors) LWE & Ring-LWE, symmetric & asymmetric cryptography. Proposes a quantum-resistant hybrid encryption scheme for IoT-enabled smart grids. Secures smart grid IoT communications against classical and quantum attacks. Computational overhead on ultra-constrained devices, evolving Post-Quantum Cryptography (PQC) standards, and a lack of large-scale real-world deployment. 34 ECC, Transport Layer Security (TLS), CPS-IoT integration. Proposes a privacy-preserving communication framework for smart city transportation. Secures data confidentiality and integrity in CPS-IoT transportation systems. Limited evaluation on real-world deployments, need for hybrid privacy techniques alongside ECC. 28 Kyber key encapsulation mechanism, Fast-Fourier Transform over Lattice-based Cryptography (FALCON) signatures, ZK-STARKs (ZKP), PQC. Quantum-Enhanced Security for Smart Meters (QESM) framework, securing smart meter-grid data with quantum-resistant key exchange, signing, and private verification. Protects smart grid data from quantum attacks; ensures confidentiality, integrity, and lightweight IoT performance. PQC overhead, scalability challenges, legacy integration, and limited real-world deployment/testing. 35 IoT automation, AES/TLS encryption, multi-factor authentication/Public Key Infrastructure auth, Advanced IDS, IPS blockchain, and physical layer security. Survey of smart home security architectures in smart cities. Enhances privacy, energy efficiency, and resilient IoT communication. Interoperability, cost, scalability, energy use, and evolving cyber threats. 36 Metamodeling, auth, IDS/IPS, encryption, secure storage. IoT Security Metamodel (IoTSM) is a unified IoT security framework for smart cities. Strengthens device, network, and data protection. Scalability, heterogeneity, legacy integration, and limited real-world validation. 37 AI/ML-IDS, Support Vector Machine, HIDS/NIDS Layered intrusion detection framework for smart cities Early attack detection protects IoT data/services Scalability, false alarms, zero-day attacks, and deployment cost 38 IDS/IPS, encryption, authentication, blockchain, AI/ML detection, digital forensics, and log analysis. Comprehensive review of cybersecurity and cyber-forensics frameworks for smart city IoT/ICT systems. Enhances threat detection, incident response, evidence collection, and infrastructure resilience. Lack of standards, interoperability issues, scalability, privacy concerns, and limited real-world forensic readiness. 39 Blockchain (DLT), smart contracts, decentralized identity, encryption, and access control. Decentralized trust framework ensuring secure data sharing and integrity across smart city IoT services. Tamper-proof records, trusted transactions, improved privacy, resilient distributed infrastructure. Blockchain latency, scalability, storage/energy overhead, integration with legacy IoT, and regulatory/privacy concerns. Open in a new tab Prior research has addressed efficiency and practicality issues in attribute-based and homomorphic encryption from multiple perspectives. The article 40 presents a Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication (FHEOKG-SGC) framework. This technique supports key generation and encryption in the IoT context. Experimental results demonstrate that FHEOKG-SGC outperforms existing approaches. The future scope includes improvements using lightweight cryptographic algorithms. The paper 41 presents an efficient pairing-free CP-ABE scheme for IoT environments. It uses lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The scheme achieves Chosen-Plaintext Attacks (CPA) security under the Elliptic Curve Decisional Diffie-Hellman (ECDDH) problem and shows better efficiency than existing methods. Performance results show higher efficiency and better suitability for IoT devices than existing schemes. The authors 42 evaluated three FHE libraries on a Raspberry Pi 4 and measured runtime, CPU usage, temperature, and memory. OpenFHE showed better CPU efficiency, SEAL achieved lower temperature and faster arithmetic, and Lattigo provided the fastest key generation. Multi-Key FHE enables secure computation over data encrypted with different keys, but existing schemes suffer from large parameter sizes and high computational costs. Article 43 provides a solution called Multi-Key Fully Homomorphic Encryption (MKFHE) based on BGV. It is an improved multi-hop system that removes auxiliary schemes, reduces key size, and simplifies key generation and switching. The design lowers overhead and offers better flexibility and scalability than existing methods. A recent article 39 has investigated secure data sharing in the IoT context using cryptographic authentication mechanisms and blockchain-assisted decentralization to protect sensitive information. This type of approach demonstrates improved security and trust management, which are essential for reliable smart city deployments. Authors in[ 44 ] have proposed a system called the signal strength exchange (SSE) system. It is a key generation system, and a synchronized quantization (SQ) method is a component of the SSE system that synchronizes data blocks during the quantization phase. The SQ method removes the signal pre-processing phase and performs a multi-bit conversion from the measured channel characteristics. Two authorized nodes apply synchronization for proper keys. The experiment on IoT devices with IEEE 802.11 radio shows lower computation time and communication overhead than existing methods. Proposed system The purpose of this experimental study in a simulated environment is to determine whether FHE and ABE are suitable for smart environment monitoring systems in smart cities, evaluate the security and efficiency of these cryptographic approaches, and provide comparative findings. In this work, a network simulator is used to implement the FHE and ABE cryptographic algorithms, and their performance is evaluated. It specifically examines how well they protect data in smart city applications, such as environmental monitoring. The security mechanisms are based on recognized cryptographic algorithms and are validated through NS-2-based simulation for comparative performance evaluation in a smart city IoT environment. A simulation model named ‘ FHE and ABE with Fast Exponentiation Optimization’ (FA-FEO) is developed in this experimental work. Model simulates an IoT network using a network simulator. Simulation environment and parameters All simulations were performed on a Linux guest operating system running under virtualization on a Windows 10 Pro (64-bit) host system equipped with an Intel Core i3-2370 M processor operating at 2.40 GHz and 8 GB of main memory. All information regarding the simulation setup is represented in Table 2 . Table 2. Simulation setup information. Parameter Values Simulator Network Simulator through a virtualization platform Number of nodes 100,125,150,175,200 Simulation area 500 × 500 MAC protocol IEEE 802.11 (Wi-Fi MAC protocol) Routing protocol SEALR (Secure and Efficient Ad hoc Location Routing ) Traffic CBR (Constant Bit Rate) Addressing Flat Cryptography FHE, KP-ABE, CP-ABE ( FHE and ABE with Fast Exponentiation Optimization (FA-FEO) simulation model) Simulation time 200s Security testing Insider attack, replay attack, packet capturing, denial-of-service (DoS), eavesdropping, packet duplication, and injection of anonymous packets. Key performance indicators Key generation timings, Energy, PDR, Latency, Execution Time, Throughput, Overheads, Memory Required Scalability Multi-node scenarios Addressing Flat Link Layer Link Layer type is LL (Logical Link Layer). Channel type Wireless channel Antenna type Omni antenna (Omnidirectional Antenna). Key size, cipher text size Integer arithmetic (uint32_t/uint64_t) Open in a new tab By modeling interactions between IoT devices, we evaluate the performance indicators, including key generation and execution times. The simulation output is intended to provide insights into performance optimization and to support the development of more practical IoT systems 45 – 48 . It revolves around the network of carefully positioned sensors designed to manage and monitor urban ecosystems. By monitoring a range of environmental factors, such as temperature, humidity, gas concentrations, and seismic activity, the sensors offer real-time insights. To ensure a comprehensive understanding of environmental dynamics, these sensors are strategically placed in residential, urban, and green spaces. A central base station controls the network by sending out announcement messages, establishing sensor updates with location data and unique IDs, and establishing bidirectional communication via spatial computations and connection messages. Numerous wireless communication modules, such as Long Range Wide Area Network (LoRaWAN), Zigbee, and Wi-Fi, can be used to set up these systems 47 . Once the network is configured, sensors regularly transmit data to the base station. The base station subsequently organizes the data into a hierarchical communication structure. This enables gateways to efficiently transfer data to user devices; ensuring customers receive relevant, up-to-date information. By serving as intermediary nodes that connect user devices to the base station, the gateways enable secure network interactions by verifying user access. When requested data is unavailable, the base station gathers new information directly from sensors, increasing the system’s flexibility and responsiveness. Figure 1 represents the complete system architecture. Fig. 1. Open in a new tab FA-FEO system architecture. Collaborative sensor communication ensures efficient data transmission by using nearby sensors as intermediaries to provide data to the base station. In addition to supporting comprehensive data collection, this cooperative approach maintains a robust communication channel. The bidirectional communication system, which utilizes routes from the base station to the user device and vice versa, ensures reliable and effective data exchange throughout the network. The hierarchical, multi-hop architecture of the system ensures a responsive, secure smart city infrastructure by facilitating smooth communication among sensors, base stations, gateways, and users. Figure 2 depicts the animated simulation of the sensor network. To obtain a metric that provides details about the sensor’s relative position to the base station, the distance D is calculated. The location change between two nodes, represented by coordinates and, is computed during the interval between and . During this period, the node’s dynamic movement or relocation is revealed by precisely identifying changes in spatial position or displacement over designated time intervals. Fig. 2. Open in a new tab Sensor network animated simulation for monitoring smart city environmental factors. A. Fully homomorphic encryption key generation and key sharing Data transfer between the base station, sensors, gateways, and users is protected by encryption. A shared encryption key is securely exchanged between the sender and the recipient before data transfer. This key ensures the security and confidentiality of the data being transferred. To establish a secure, authenticated communication channel, the sender retrieves the receiver’s shared key from the key table. This ensures that encrypted data is being exchanged between the devices. To encrypt the data, the shred key is retrieved. After the key exchange, the encryption process begins. Once the key has been verified in the key table, data is encrypted using the FHE key. If the key is missing, the FHE key-sharing process is started. This process involves setting polynomial parameters, generating secure values using a random number generator, verifying the cryptographic strength of prime numbers, and estimating primitive roots to enhance key security. The unique characteristic of FHE enables computations on encrypted data without decryption, ensuring privacy and security throughout key sharing and data transport. ‘ Fast exponentiation’ and ‘ primality’ are two techniques used in the implementation of a cryptographic key generation algorithm. Variables like , , , and are initialized at the start of the process. It effectively computes the modular exponentiation result based on the binary representation of , using a customized ‘FastExponentiation’ function. The variable is iteratively reduced by half until it equals zero. After the exponentiation procedure, the primality test in algorithm (1) is performed using the probabilistic Miller-Rabin algorithm, which iterates to improve accuracy. Algorithm 1. Open in a new tab Algorithm: PrimalityTest If the variable meets certain conditions, the method returns value 1, indicating the probability of a prime number. If not, a loop runs times, updating , with the ‘FindTestElements’ function shown in algorithm (2) and looking for circumstances that would signify the potential primality of the tested number. Algorithm 2. Open in a new tab FindTestElements The function ‘FastExponentiation’ effectively squares the base at each stage while methodically including every bit of the binary exponent into computation 7 . This method greatly reduces the number of multiplications compared to naive exponentiation, making it ideal for large modular exponentiation. After that, the execution enters a loop to produce a random odd number; called , ensuring that it is prime using the ‘PrimalityTest’ function. The condition of co-primality with is then satisfied by randomly generating a private key between and . The ‘FindT’ function calculates the public key . Once all key generation calculations are over, the procedure ends by confirming that the created keys contain no negative values. Crucially, in the event of an extended iteration count, the algorithm recommends potential exit points from the loops. In all, the approach provides a foundation for generating cryptographic keys while balancing efficiency and security. To guarantee accuracy and efficiency in cryptographic operations, the polynomial degree is limited to , establishing a defined range of coefficients. To balance computational efficiency and stability, a random number generator operates within this range. A homomorphic probabilistic algorithm that evaluates compositeness reinforces a rigorous primality test that verifies the resulting number’s eligibility for cryptographic usage. Using random number selection and iterative modulo computations, the algorithm improves its confidence in identifying primes and smoothly incorporates them into the polynomial framework for cryptographic operations. After identifying prime numbers, the algorithm computes the modulus exponent using the greatest common divisor (GCD) to produce the encryption key, which includes the modulus, exponent, and polynomial degree. Generated key is safely stored and shared with the receiver while enabling FHE. The FHE mechanism ensures privacy and confidentiality during cryptographic operations by enabling computations on encrypted data without decryption. The FHE key encrypts the raw data, or sensed data, enabling homomorphic computations over the encrypted data. The encrypted data is sent wirelessly after successful completion of homomorphic operations such as addition and multiplication. A critical integrity check initiates the regeneration and subsequent sharing of the FHE key between devices in the case of a failed homomorphic operation. To maintain the system’s homomorphic property and ensure secure transfer to the destination device, encrypted data is sent via a multi-hop communication framework with relay nodes. The receiver retrieves the appropriate homomorphic decryption key from the key table after receiving the data. This key enables decryption, protecting data privacy and secrecy throughout the transfer. To recover the original message, the decrypt function stores the decrypted characters in an array after processing the arrays and using the private key and modulus . As the algorithm iterates over the ciphertext, decrypts each component, and appends it to the original message array, a null terminator is added for correct message termination. In case decryption fails, the system ensures reliable, secure data sharing by identifying and addressing flaws. Further, the scheme supports homomorphic addition and bounded-depth multiplication with explicit noise tracking and a threshold-based refresh mechanism. Each ciphertext consists of two 32-bit components (C1, C2), yielding a 64-bit ciphertext per 32-bit plaintext element (≈ 2× expansion ratio). The design prioritizes the feasibility of IoT simulation and does not implement full lattice-based FHE features, such as modulus switching or bootstrapping 7 . B. Attribute-based encryption In ABE, data access is controlled by the attributes assigned to entities. The attributes are used to construct access policies that allow precise control over who can decrypt data. Every user has a unique set of attributes that indicate their identity or function. These access controls are necessary for data encryption to ensure that the data can only be decrypted by those who possess the required attributes 49 . This attribute-driven strategy enhances flexibility by enabling dynamic, customized access control that aligns with the unique features of the system’s entities. The purpose of the ABE key generation technique presented in algorithm (3) is to provide cryptographic keys for attribute-based encryption. After identifying the ABE scheme (KP-ABE or CP-ABE) and obtaining the two key types ( kpabe_key and cpabe_key ), the algorithm generates the required public and private keys .The algorithmic phases include initializing iteration counters and arrays to store KP-ABE and CP-ABE keys during ABE key generation. Flags monitor key retrieval success, and the encryption method is chosen based on retrieved keys. The Miller-Rabin algorithm is used to confirm the primality of a randomly generated prime number greater than 512. Similarly, the primality of an odd number is verified. Next, a private key is selected from the range to ensure that it is co-prime with . The ‘FindT’ function computes the public key e2. The procedure is repeated if there are any negative values. Algorithm 3. Open in a new tab KeyGenerationABE Rules over attributes are used to construct access policies in the KP-ABE, a type of ABE. According to this approach, communication entities possess secret keys that are closely associated with access policies defined by their respective attribute sets. A proper and efficient access control system is ensured by matching secret keys with user attributes. This highlights the personalized, attribute-driven nature of encryption and access control procedures within the ABE paradigm. The successful alignment of the user’s key with the access policy, defined by these specific attributes, enables a secure, tailored decryption process. Variables for these parameters are initialized, and character arrays are set up to hold their string representations. To construct dynamic components of the key, random values are generated for the base, class, and third octets of an IP address. To create the final key, these IP addresses are turned into strings and combined with the connection time. The process returns an error (-1) if the created key’s length is zero. Otherwise, the algorithm completes and outputs a character array containing the generated key. To create a safe, context-aware encryption framework, the encryption technique leverages device-specific attributes. Key generation and access policy formulation are based on attributes such as device identifier (nodeID), Connection IP (connIP), gateway IP (gwIP), and connection initiation timestamp (c_time). These attributes are thoughtfully organized into string octets, guaranteeing that device identity, network connectivity, and temporal context are all effectively included. All of these elements are embedded in the generated key, which enables the adapted encryption technique that corresponds to the defined access policy. The generated key protects the data during encryption, and integrity is ensured by verifying the key. To strengthen the encryption architecture, the key generation procedure is repeated whenever validation fails, thereby exposing potential vulnerabilities or inconsistencies. By enhancing the encryption process and addressing potential flaws through ubiquitous validation and improved key management, this iterative approach underscores the importance of data security. One more type of ABE, CP-ABE, enables flexible, responsive data encryption by incorporating access policies into ciphertext. Users receive secret keys associated with their attributes, thereby enabling policy-matching decryption. Unlike conventional encryption techniques, access rules are incorporated into the ciphertext. Since the access policy is an intrinsic component of the encrypted data, this technique enables more flexible, granular access control. Users can decrypt the data that satisfies the access policies associated with ciphertexts by receiving secret keys with matching attributes. This innovation enables a more granular and versatile access control mechanism, as the access policy is an inherent part of the encrypted data itself. Three different metrics are used in the formulation of policy. The association between nodeID and connectionIP (p1) , gatewayIP and connectionIP (p2) , and nodeID and gatewayIP (p3) is represented by these metrics. These metrics are assessed and managed using a particular approach as part of the control computation process. Integrating policy parameters into ciphertext to facilitate decryption and integrity checks is an essential component of data transport. The encryption key is regenerated, and the data is resent if the integrity check fails. This technique guarantees reliability and flexibility in communication. Results and discussion In the proposed model, the optimization technique is meticulously incorporated into the key generation process, especially when selecting domain inputs. To expedite the key generation process and to reduce repeated polynomial computations, a particular optimization approach called ‘ fast exponentiation’ is used. The time needed for key generation is significantly reduced by this ‘ fast exponentiation’ technique, which is fairly effective 7 . It not only speeds up the generation of FHE keys but also reduces processing delays in the communication framework’s encryption and decryption processes. This enhanced key generation process has an influence that extends beyond the system’s responsiveness and performance. The key generation process becomes more time- and resource-efficient through the thoughtful application of an optimization technique, enabling faster, more responsive cryptographic operations within the communication network. In this study, evaluating timestamps across different metrics, the assumption is that timestamps can vary depending on the system configuration and the simulation speed. Also, there may be differences in timings during real-time implementation, as these results are taken in a simulation context. Our previous study 7 compares the results obtained with three encryption methods, such as CP-ABE, KP-ABE, and FHE, in a simulated environment. Each has been assessed both with and without ‘ fast exponentiation’ and benchmarked against a custom BASE encryption approach. Investigation 7 considers performance metrics such as consumed energy, power consumption, PDR (Packet Delivery Ratio), latency, execution time, throughput, overheads, jitter, number of dropped packets, memory required, and resource utilization. The statistical analysis of the implemented model, comparing the outcomes of four cryptographic algorithms (CP-ABE, KP-ABE, FHE, and a non-optimized BASE scheme) across varying node counts, is presented in prior work. The implementation uses a ‘fast exponentiation’ optimization to improve system performance, leveraging FHE and ABE. According to our previously reported study 7 CP-ABE shows the stable and efficient performance across all network sizes, while the BASE scheme exhibits high variability and limited scalability. ‘ Fast exponentiation ’ significantly reduces energy consumption and lowers latency, making CP-ABE and KP-ABE suitable for real-time, resource-constrained smart city IoT applications. KP-ABE can be used for lightweight, scalable IoT systems, as its results are comparable to those of CP-ABE. Although FHE enables computations over encrypted data, its high computational cost prevents real-time use without optimization. The incorporation of ‘ fast exponentiation’ significantly reduces execution time and energy consumption, improving FHE’s feasibility for privacy-preserving, resource-constrained IoT applications. In our proposed study, we assess performance and practical feasibility for KP-ABE, CP-ABE, and FHE. Comparative evaluation of all three is performed under the same runtime environment and context. All experiments are conducted in a simulation setup to ensure controlled conditions and fair comparison among the schemes. Conversely, authors in[ 42 ] implemented on a Raspberry Pi using standard FHE libraries and reported hardware-level metrics, including key generation time, execution time, power consumption, and memory usage. In FHE using SEAL, both encryption and decryption time increase as the polynomial degree and coefficient modulus size increase 50 . By using simulation and simplified cryptographic implementations, our approach reduces computational complexity and achieves better FHE performance. However, in real-time hardware deployments, the actual metric values may vary depending on system resources and device constraints. Table 3 compares the results of the proposed work with existing techniques in the domain of IoT/WSN security. Table 3. Comparative analysis of existing schemes for IoT/WSN security. Reference Scheme / technique Platform/ context Security level Key generation performance / cost Measured metrics Optimization strategy Relevance to IoT/WSN 43 Multi-Key FHE Enhancements (2025) based on Brakerski-Gentry- Vaikuntanathan (BGV) Software multi-party setting RLWE-based Complexity only Evaluation key size reduced to approximately ¼ th of the existing studies. switching cost O(n); fewer multiplications Eliminates need for auxiliary Gentry-Sahai-Waters (GSW); efficient key switching; batching Secure multiparty cloud/edge 42 FHE Benchmarking Libraries (2025) (OpenFHE/SEAL/Lattigo). Raspberry Pi 4 (Quad-core 1.8 GHz, 8 GB RAM) 128-bit Measured (ms scale, <1s) CPU: 24–129% RAM: 222–306 MB Power: 4.3–5.7 W Temp: 41–49 °C Ciphertext: 334–2150 kB Serialisation: 4–31 ms Library selection, multithreading, lightweight configs Direct IoT edge evaluation 41 Pairing-Free CP-ABE (2024) Software implementation, crypto experiments 128-bit Not reported Lower pairing cost, smaller computation Eliminates bilinear pairings Suitable for constrained devices 40 Optimal FHE Key Generation (2023) Desktop/server crypto setting 128-bit+ Not reported Theoretical cost/parameter reduction Optimal key structure, reduced asymptotic cost Conceptual for secure systems 44 Efficient SSE Key Generation (2019) Low-power IoT wireless nodes Variable key length (≈ 128-bit achievable) 2.03–8.21 s Reduced communication overhead, key synchronized rate: ≈99–100%, key discrepancy rate: very low, Removes pre-processing + error correction Uses synchronized quantization Specifically designed for IoT Proposed work FA-FEO model FHE, KP-ABE, CP-ABE comparative performance evaluation IoT nodes communication using network simulation. Resource-constrained sensor devices. An integer-based modular arithmetic simulation model based on FHE principles for IoT feasibility evaluation. (uint32_t/uint64_t) Measured in milliseconds (less than 1s) Key generation timings, Energy, PDR, Latency, Execution Time, Throughput, Overheads, Memory Req. Fast exponentiation optimization for key generation. Lightweight parameter selection for IoT feasibility Smart city IoT sensor network. Open in a new tab Table 4 presents the average key generation timestamps for the evaluated schemes. The graphical results shown in Fig. 3 further reinforce the performance differences observed by applying ‘ fast exponentiation’ across the evaluated cryptographic schemes. In the case of ‘ fast exponentiation’ , the plot shows a clear reduction in key generation time for KP-ABE, CP-ABE, and FHE compared to their naive exponentiation variants. With an average time of 672 milliseconds, the CP-ABE scheme marginally outperforms the KP-ABE system, which has an average key generation time of 693 milliseconds. With a key generation time of 724 milliseconds, the FHE technique follows KP-ABE. These times show the relative performance of KP-ABE, CP-ABE, and FHE in key generation speed, reflecting the computational efficiency of each encryption technique. Table 4. Average key generation timestamps. Technique KP-ABE CP-ABE FHE Average key generation time (ms) with Naive Exponentiation 20,720 20,797 22,483 Average key generation time (ms) with Fast Exponentiation 693 672 724 Open in a new tab Fig. 3. Open in a new tab Comparative analysis of average timestamps with Fast Exponentiation. In contrast, the graph depicting naive exponentiation (Fig. 4 ) reveals the additional computational cost of using non-optimized exponentiation routines. KP-ABE and CP-ABE exhibit nearly identical performance − 20.72s and 20.80s, respectively, reflecting their similar resilience on large modular arithmetic. FHE shows the highest latency at 22.48s, illustrating significant computational overhead. Collectively, two graphs visually demonstrate the transformative effect of ‘ fast exponentiation’ , reducing key generation time across all three cryptographic techniques. Fig. 4. Open in a new tab Comparative analysis of average timestamps with Naive Exponentiation. Scalability considerations and deployment challenges The proposed system is the only system to provide a comparative performance analysis of all three methods within the same simulated runtime environment and IoT context. Simulations have enabled us to achieve positive results. The system presents a lightweight integer-based modular arithmetic simulation model inspired by FHE principles for IoT feasibility evaluation. In the future, the proposed system could be implemented in hardware, thereby completing the same technical effect and expanding applicability in real-time IoT environments. In a real-world context, however, cryptographic library support is required, which is not feasible for FHE and ABE in the network simulation (NS-2) environment. These techniques cannot be directly implemented in large-scale NS-2 simulations because of computational constraints. To address this, we used a lightweight emulation technique. It models cryptographic activities, including key generation, encryption, access policy enforcement, homomorphic operations, and decryption using common integer types (uint32_t and uint64_t). This approach preserves simulation scalability and enables exploration of effects on network performance, such as latency, throughput, and energy consumption. This comprehensive comparison underscores the uniqueness and importance of our approach for advancing IoT security research. The implementation supports bounded-depth homomorphic computation. The prototype is intended for architectural and performance trend analysis rather than production-grade FHE deployment. Scalability of the proposed system is analyzed in a simulation environment using multi-node scenarios. Results are generated for a varying number of IoT nodes (100, 125, 150, 175, and 200). All cryptographic schemes are tested under the same runtime environment and context. The results show stable performance as the network size increases, indicating good scalability for large IoT deployments. The proposed system has been validated through a working prototype and shows strong feasibility for smart city use cases such as environmental monitoring. The results show effective performance across a varying number of nodes. The optimization strategy is included to reduce overhead and improve performance. Hence, the approach can be suitable for real-time deployments. However, large-scale real-time environments may include challenges such as congestion, delay, and increased network traffic due to a high number of devices. Device heterogeneity and differences in communication protocols may create operational and protocol compatibility issues. In addition, security mechanisms and complex standard libraries may add more computational overhead. Ultra-large smart city deployments may involve thousands or even millions of nodes. The current study evaluates up to 200 nodes to ensure deterministic event logging, reproducibility, and detailed security monitoring within NS-2. Although real-world smart city infrastructures can scale to thousands or millions of IoT nodes, the present study is constrained by simulation configuration and computational limitations. Although NS-2 can handle up to several thousand nodes, in practice, its performance degrades, and memory demands grow rapidly as node counts increase. Therefore, further optimization may be required to maintain performance at scale. Overall, the system remains practical and scalable, and these challenges provide future directions for further improvements. Threat model and adversarial assumptions A thorough verification procedure is applied to strengthen network communication against possible attacks. This accounts for many types of attacks, including packet capture, eavesdropping, insider attacks, denial-of-service (DoS) attacks, and replay attacks 7 . The network provides an effective defense against many security breaches by integrating these attack scenarios into the verification process. Potential attackers strategically position malicious nodes within the network to interfere with authorized communication channels. This approach ensures that only verified, legitimate entities participate in data exchange, thereby maintaining the confidentiality and reliability of the network. The identified attacker is quickly removed from the network. It ensures the reliability and safety of the communication channels. Additionally, information about the attacker is broadcast to other devices to maintain a collective defense against potential threats. By mitigating the effects of various attacks and fostering a reliable network environment, this cooperative solution aims to establish a secure, resilient communication infrastructure. Conclusion and future work In conclusion, adopting KP-ABE, CP-ABE, and simplified FHE techniques provides a secure and efficient means of data protection in smart city applications, particularly for environmental monitoring. FHE’s basic functionality is implemented without external cryptographic libraries due to its intrinsic computational complexity, which requires significant resources to process. Instead, to enable controlled performance evaluation, a customized FHE prototype was developed in an IoT network simulation environment. This configuration enabled a fair comparison of key generation times between KP-ABE and CP-ABE. The goal was to determine whether the computational cost of key generation in FHE, even in a simplified scenario, is comparable to or exceeds that of ABE schemes under similar conditions. These cryptographic techniques ensure data privacy and confidentiality and provide flexible attribute-based access control. The optimization method reduces cryptographic delays and accelerates key generation. Despite computational efficiency issues, the results show that these methods offer a potential basis for developing smart city infrastructure. According to performance analysis, the modified key generation procedure increases system efficiency. Further investigation can enhance the relevance of FHE and ABE in evaluating and analyzing their performance in real time. Accordingly, extensive distributed simulations and ultra-large-scale real-world testbed validation are important directions for future research. Data Availability No specific dataset was used for the research described in the article. The data used for the graphs will be made available by the author, K. D. M., upon request. Code Availability Article-specific code of the proposed FA-FEO model is available at 10.5281/zenodo.19087822. For further information or specific inquiries regarding the code, please contact the author, K. D. M. Author contributions K.D.M. Writing - original draft preparation, writing - review and editing, visualisation, validation, methodology, investigation, formal analysis, conceptualization. D.P. Writing - review and editing, supervision, methodology, and conceptualization. All authors have read and agreed to the published version of the manuscript. Funding Open access funding provided by Symbiosis International (Deemed University). 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