IEEE INTERNET OF THINGS JOURNAL, VOL. 6, NO. 2, APRIL 2019 3309 EPIC: Efficient Privacy-Preserving Scheme With EtoE Data Integrity and Authenticity for AMI Networks Ahmad Alsharif , Member, IEEE , Mahmoud Nabil, Samet Tonyali, Hawzhin Mohammed, Mohamed Mahmoud , Member, IEEE , and Kemal Akkaya, Senior Member, IEEE Abstract —In this paper, we propose EPIC, an efficient power consumption during peak hours using dynamic pricing and privacy-preserving data collection scheme with EtoE data approach in which the electricity prices may change dur- integrity verification for advanced metering infrastructure ing the day to encourage consumers to reduce their power networks. Using efficient cryptographic operations, each meter consumption. should send a masked reading to the utility such that all the However, the fine-grained power consumption readings can masks are canceled after aggregating all meters’ masked read- reveal sensitive information about the consumers’ activities, ings, and thus the utility can only obtain an aggregated reading to preserve consumers’ privacy. The utility can verify the aggre- such as the times consumers leave/return homes, as well as, gated reading integrity without accessing the individual readings the appliances they use since each appliance has a unique to preserve privacy. It can also identify the attackers and com- power consumption signature [3]–[5]. Privacy-preserving data pute electricity bills efficiently by using the fine-grained readings aggregation is a promising technique to enable the utility without violating privacy. Furthermore, EPIC can resist collu- to obtain an aggregated fine-grained reading from an AMI sion attacks in which the utility colludes with a relay node to extract the meters’ readings. A formal proof and probabilistic network without learning the individual readings to preserve analysis are used to evaluate the security of EPIC, and ns-3 is the consumers’ privacy. However, the existing schemes, such used to implement EPIC and evaluate the network performance. as [6]–[10], extensively use asymmetric-key cryptography in In addition, we compare EPIC to existing data collection schemes data aggregation, which typically involves large computation in terms of overhead and security/privacy features. and communication overhead. They also do not address end- Index Terms —Advanced metering infrastructure (AMI) to-end (EtoE) data integrity in which the utility can ensure networks, and dynamic pricing, collusion resistance, data that all the individual fine-grained readings are not altered integrity, privacy preservation, smart grid. during transmission and aggregation without accessing the individual readings to preserve privacy. Moreover, they do not address EtoE authenticity in which the utility can ensure I. I NTRODUCTION that the aggregated reading is computed using the fine-grained T HE SMART grid initiative aims to develop a clean, readings coming from intended consumers. Furthermore, gen- reliable, and efficient system. It extensively integrates erating electricity bills using the reported fine-grained readings information technology into the power grid [1]. One main based on dynamic prices is challenging since the utility should component of the smart grid is the advanced metering infras- not have access to the fine-grained readings to preserve pri- tructure (AMI) networks that connect smart meters (SMs) vacy, but these readings are needed to generate consumers’ installed at consumers’ side to the electric service provider bills. (the utility). SMs should send fine-grained power consump- In this paper, we propose an efficient privacy-preserving tion readings to the utility to perform real-time monitoring and scheme with EtoE data integrity, authenticity, and collusion- energy management [2]. Moreover, the utility can reduce the resistance for AMI networks (EPIC). The idea is that each SM selects a number of SMs in the network called “proxies” and Manuscript received August 28, 2018; revised October 25, 2018; accepted November 12, 2018. Date of publication November 21, 2018; date of efficiently computes shared pairwise secret masks with each current version May 8, 2019. This work was supported by the U.S. proxy. Then, it should mask its fine-grained reading with all National Science Foundation under Grant CNS-1619250. (Corresponding the masks shared with the proxies, such that all the masks author: Ahmad Alsharif.) A. Alsharif is with the Department of Computer Science, University of are canceled after aggregating all meters’ masked readings, Central Arkansas, Conway, AR 72035 USA, and also with the Department of and thus the utility can only obtain an aggregated reading to Electrical and Computer Engineering, Tennessee Tech University, Cookeville, preserve consumers’ privacy. EPIC can also resist collusion TN 38505 USA (e-mail: aalsharif@uca.edu). M. Nabil, H. Mohammed, and M. Mahmoud are with the Department attacks in which the utility can collude with a relay meter to of Electrical and Computer Engineering, Tennessee Tech University, extract a meter’s fine-grained readings because readings are Cookeville, TN 38505 USA (e-mail: mnmahmoud42@students.tntech.edu; masked by several secret masks shared with a number of dif- hmohammed42@students.tntech.edu; mmahmoud@tntech.edu). S. Tonyali and K. Akkaya are with the Department of Electrical and ferent proxies. The number of the selected proxies controls Computer Engineering, Florida International University, Miami, FL 31174 the protection level against collusion attack. In addition, to USA (e-mail: stony002@fiu.edu; kakkaya@fiu.edu). ensure EtoE data integrity and authenticity, a homomorphic Digital Object Identifier 10.1109/JIOT.2018.2882566 2327-4662 c � 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. 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