SLICER: A Slicing-Based K-Anonymous Privacy Preserving Scheme for Participatory Sensing

Abstract

With the popularity of mobile wireless devices with various kinds of sensing abilities, a new service paradigm named Participatory Sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy preservation and multimedia data participatory sensing are two critical problems. Unfortunately, none of the existing works has fully solved the problem of privacy preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message exchanging strategies, to achieve strong protection of participants' privacy, while maintaining high data accuracy. In addition, two slice transferring strategies are well designed for slice transfer to minimize the total transfer cost. Finally, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data accuracy, with low computation and communication overhead.

An effective value swapping method for privacy preserving data publishing

“…There are extensively work performed to address the information disclosure for statistical databases. Some popular anonymization techniques have been proposed to protect the published microdata from information disclosure risks. In , the authors proposed k‐ anonymity approach where a QI group is said to satisfy k‐ anonymity if the size of the QI group is at least k .…”

“…To increase the data utility and the privacy, recently, the slicing method and its variants have been proposed for anonymizing the microdata. There is another slicing technique that is related to sensor data publishing but not being related to relational data publishing. In , a location privacy preservation method was proposed for cognitive radio networks to protect the location privacy with maximizing the data utility, while suggests a trade‐off between individual privacy and system performance for cyber‐physical systems.…”

User privacy and data trustworthiness in mobile crowd sensing

“…Unfortunately, due to this increase, the existing privacy preserving schemes are not suitable for MCS applications involving multimedia data because high communication cost would be incurred or the utility/accuracy of the data would be degraded. Qiu et al [15] addressed this problem by developing a k-anonymous privacy preserving scheme, SLICER, for MCS applications with multimedia data. Based on erasure coding and efficient slice transfer strategies, SLICER achieves strong privacy preservation and high tolerance to data loss, and also has low computation and communication overhead.…”

“…Based on erasure coding and efficient slice transfer strategies, SLICER achieves strong privacy preservation and high tolerance to data loss, and also has low computation and communication overhead. However, some related issues, such as privacy preservation in the query process, were not considered in [15].…”

A Collusion-Resistant and Privacy-Preserving Data Aggregation Protocol in Crowdsensing System

“…Therefore, the user's security and privacy should be protected in a crowdsensing system. Lots of previous works [5][6][7][8][9][10][11] have focused on the challenge. Specifically, [5] allows the server to evaluate any multivariate polynomials.…”