Abstract
Many miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confidentiality mechanism. However, those works either suffer from collusive attack or require time-consuming initialization at every aggregation request. In this paper, we proposed an efficient aggregation protocol which tolerates up to k passive adversaries that do not try to tamper the computation. The proposed protocol does not require a trusted key dealer and needs only one initialization during the whole time-series data aggregation. We formally analyzed the security of our protocol and results showed that the protocol is secure if the Computational Diffie-Hellman (CDH) problem is intractable. Furthermore, the implementation showed that the proposed protocol can be efficient for the time-series data aggregation.
Highlights
The security and privacy issue in pervasive computing applications, such as mobile cloud computing, crowd sourcing, and smart metering, has long been a hot research topic in the field of applied cryptography
We show that any Probabilistic Polynomial Time Adversary (PPTA) that has significant chance to infer private values in our Setup phase has nonnegligible advantage to solve the Computational Diffie-Hellman (CDH) problem, which is a contradiction to our security assumption that CDH problem is intractable
We proposed a privacy-preserving aggregation scheme for time-series data without trusted key dealers
Summary
The security and privacy issue in pervasive computing applications, such as mobile cloud computing, crowd sourcing, and smart metering, has long been a hot research topic in the field of applied cryptography. Smart meters report consumption for users at high frequency (e.g., once per minute) and in real time This level of monitoring can reveal much private information about users’ habits and subject the users to many loathsome outcomes [1, 2], for example, whether they often watch TV (discriminating pricing of health insurance), or even stealthy surveillance in general [3]. For another example, mobile users report their locations, speeds, and mobility to a GPS service provider at real time. The individual information above needs to be protected in the privacy consideration
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