Abstract
As smart cities and nations are fast becoming a reality, so does the underpinning infrastructure, such as smart grids. One particular challenge associated with smart grid implementation is the need to ensure privacy preserving multisubset data aggregation. Existing approaches generally require the collaboration of a trusted third party (TTP), which may not be practical. This also increases the threat exposure, as the attacker can now target the TTP who may be servicing several smart grid operators. Therefore, in this article, a fault-tolerant multisubset data aggregation scheme is proposed. Our scheme aggregates the total electricity consumption value, and obtains the number of users and the total electricity consumption in different numerical intervals, without relying on any TTP. Detailed system analysis shows that our scheme prevents the leakage of single data, as well as guarantees the efficiency when new user joins and existing user leaves. Findings from our evaluation also demonstrate that system robustness is achieved with negligible cost.
Highlights
A S our society becomes ‘smarter’, more of our infrastructure will transition to smart, Internet-connected systems
In [7], for example, a privacy-preserving data aggregation scheme based on differential privacy to resist human-factor-aware attacks was presented
OUR PROPOSED FTMA SCHEME our fault-tolerant multi-subset aggregation (FTMA) scheme without trusted third party (TTP) is presented, which consists of five phases: system setup, individual data encryption, cipher aggregation, aggregated cipher encryption, and fault tolerance mechanism – see Fig. 2
Summary
A S our society becomes ‘smarter’, more of our infrastructure (including those in the critical infrastructure sectors) will transition to smart, Internet-connected systems. In [7], for example, a privacy-preserving data aggregation scheme based on differential privacy to resist human-factor-aware attacks was presented. In [18], a privacy-preserving truth discovery scheme was introduced, which relies on two non-colluding cloud platforms in the crowd sensing system It is not designed for situations where users need to join and exit frequently. In [24], a human-factor-aware privacy-preserving scheme designed to resist human-factoraware differential aggregation (HDA) attacks was proposed. Ni et al [26] presented an efficient data aggregation without involving any TTP, and user’s privacy is preserved even if all entities in the system are not fully trusted.
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