PPETD: Privacy-Preserving Electricity Theft Detection Scheme with Load Monitoring and Billing for AMI Networks

Mahmoud Nabil, Muhammad Ismail, Mohamed M.E.A. Mahmoud, Waleed Alasmary, Erchin Serpedin

Research output: Contribution to journalArticle

2 Citations (Scopus)


In advanced metering infrastructure (AMI) networks, smart meters installed at the consumer side should report fine-grained power consumption readings (every few minutes) to the system operator for billing, real-time load monitoring, and energy management. On the other hand, the AMI networks are vulnerable to cyber-attacks where malicious consumers report false (low) electricity consumption to reduce their bills in an illegal way. Therefore, it is imperative to develop schemes to accurately identify the consumers that steal electricity by reporting false electricity usage. Most of the existing schemes rely on machine learning for electricity theft detection using the consumers' fine-grained power consumption meter readings. However, this fine-grained data that is used for electricity theft detection, load monitoring, and billing can also be misused to infer sensitive information regarding the consumers such as whether they are on travel, the appliances they use, and so on. In this paper, we propose an efficient and privacy-preserving electricity theft detection scheme for the AMI network and we refer to it as PPETD. Our scheme allows system operators to identify the electricity thefts, monitor the loads, and compute electricity bills efficiently using masked fine-grained meter readings without violating the consumers' privacy. The PPETD uses secret sharing to allow the consumers to send masked readings to the system operator such that these readings can be aggregated for the purpose of monitoring and billing. In addition, secure two-party protocols using arithmetic and binary circuits are executed by the system operator and each consumer to evaluate a generalized convolutional-neural network model on the reported masked fine-grained power consumption readings for the purpose of electricity theft detection. An extensive analysis of real datasets is performed to evaluate the security and the performance of the PPETD. Our results confirm that our scheme is accurate in detecting fraudulent consumers with privacy preservation and acceptable communication and computation overhead.

Original languageEnglish
Article number8746794
Pages (from-to)96334-96348
Number of pages15
JournalIEEE Access
Publication statusPublished - 1 Jan 2019



  • dynamic billing
  • electricity theft detection
  • machine learning
  • Privacy preservation
  • secure multi-party computation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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