EPIC: Efficient Privacy-Preserving Scheme With EtoE Data Integrity and Authenticity for AMI Networks
| Autor: | Mohamed E. Mahmoud, Samet Tonyali, Mahmoud Nabil, Ahmad Alsharif, Kemal Akkaya, Hawzhin Mohammed |
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| Rok vydání: | 2019 |
| Předmět: |
021110 strategic
defence & security studies Data collection Computer Networks and Communications business.industry Computer science Node (networking) Data_MISCELLANEOUS 020208 electrical & electronic engineering 0211 other engineering and technologies Cryptography 02 engineering and technology Computer security computer.software_genre Computer Science Applications Hardware and Architecture Data integrity Signal Processing 0202 electrical engineering electronic engineering information engineering Overhead (computing) Network performance business computer Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 6:3309-3321 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2018.2882566 |
| Popis: | In this paper, we propose EPIC, an efficient and privacy-preserving data collection scheme with EtoE data integrity verification for advanced metering infrastructure networks. Using efficient cryptographic operations, each meter should send a masked reading to the utility such that all the masks are canceled after aggregating all meters’ masked readings, and thus the utility can only obtain an aggregated reading to preserve consumers’ privacy. The utility can verify the aggregated reading integrity without accessing the individual readings to preserve privacy. It can also identify the attackers and compute electricity bills efficiently by using the fine-grained readings without violating privacy. Furthermore, EPIC can resist collusion attacks in which the utility colludes with a relay node to extract the meters’ readings. A formal proof and probabilistic analysis are used to evaluate the security of EPIC, and ns-3 is used to implement EPIC and evaluate the network performance. In addition, we compare EPIC to existing data collection schemes in terms of overhead and security/privacy features. |
| Databáze: | OpenAIRE |
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