Privacy, Security, and Utility Analysis of Differentially Private CPES Data
Md Tamjid Hossain, Shahriar Badsha, Haoting Shen

TL;DR
This paper investigates the balance of privacy, security, and utility in differentially private smart grid data, proposing a design strategy that minimizes attack impact while maintaining data utility and privacy.
Contribution
It introduces a provable relationship among DP parameters for fault-tolerant defense against false data injection attacks in smart grids.
Findings
The proposed design reduces attack impact by calibrating DP parameters.
The DP mechanism is feasible and maintains satisfactory QoS in adversarial settings.
Simulation confirms the effectiveness of the defense strategy.
Abstract
Differential privacy (DP) has been widely used to protect the privacy of confidential cyber physical energy systems (CPES) data. However, applying DP without analyzing the utility, privacy, and security requirements can affect the data utility as well as help the attacker to conduct integrity attacks (e.g., False Data Injection(FDI)) leveraging the differentially private data. Existing anomaly-detection-based defense strategies against data integrity attacks in DP-based smart grids fail to minimize the attack impact while maximizing data privacy and utility. To address this challenge, it is nontrivial to apply a defensive approach during the design process. In this paper, we formulate and develop the defense strategy as a part of the design process to investigate data privacy, security, and utility in a DP-based smart grid network. We have proposed a provable relationship among the…
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Taxonomy
TopicsSmart Grid Security and Resilience · Internet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection
