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<p>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 - <i>FHE and ABE with Fast Exponentiation Optimization (FA-FEO).</i> 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 - <i>fast exponentiation</i> considerably improved efficiency, reducing key generation time from approximately 20–22&#xa0;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.</p>

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Privacy and security enhancement in smart cities using advanced cryptographic techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Kirti Dinkar More,
  • Dhanya Pramod

摘要

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.