PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Shi, Jing; Zhang, Rui; Liu, Yunzhong; Zhang, Yanchao

doi:10.1109/infcom.2010.5462147

Cited by 186 publications

(137 citation statements)

References 20 publications

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“…There are many existing works (e.g., [17]) on privacy-preserving data aggregation, but most of them assume a trusted aggregator. Shi et al [18] proposed an aggregation scheme for mobile sensing, but the scheme does not consider time-series data. The two constructions [19,20] based on additive homomorphic encryption [17] do not guarantee differential privacy.…”

Section: Related Workmentioning

confidence: 99%

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

Cao

2013

Privacy Enhancing Technologies

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Abstract. Aggregate statistics computed from time-series data contributed by individual mobile nodes can be very useful for many mobile sensing applications. Since the data from individual node may be privacy-sensitive, the aggregator should only learn the desired statistics without compromising the privacy of each node. To provide strong privacy guarantee, existing approaches add noise to each node's data and allow the aggregator to get a noisy sum aggregate. However, these approaches either have high computation cost, high communication overhead when nodes join and leave, or accumulate a large noise in the sum aggregate which means high aggregation error. In this paper, we propose a scheme for privacy-preserving aggregation of time-series data in presence of untrusted aggregator, which provides differential privacy for the sum aggregate. It leverages a novel ring-based interleaved grouping technique to efficiently deal with dynamic joins and leaves and achieve low aggregation error. Specifically, when a node joins or leaves, only a small number of nodes need to update their cryptographic keys. Also, the nodes only collectively add a small noise to the sum to ensure differential privacy, which is O(1) with respect to the number of nodes. Based on symmetric-key cryptography, our scheme is very efficient in computation.

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Section: Related Workmentioning

confidence: 99%

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

Cao

2013

Privacy Enhancing Technologies

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“…The concepts of data slicing and mixing are used in Shi et al (2010) to support statistical additive and non-additive aggregation functions. For additive aggregation functions, the key idea is that each node slices its data into n C 1 slices.…”

Section: Data Aggregationmentioning

confidence: 99%

“…The work in Erfani et al (2013) tries to mitigate the shortcomings of the approach in Shi et al (2010). The aggregator and the mobile nodes are assumed to be untrusted.…”

Section: Data Aggregationmentioning

confidence: 99%

Privacy, Trust and Incentives in Participatory Sensing

Riahi

Rahman

Aberer

2016

Understanding Complex Systems

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“…Our work builds upon two main domains, in order to provide the privacy and incentives for the users and data aggregators: (1) privacy-preserving aggregation [14,36,37,46], and (2) privacy-preserving monetization of user profiles [4,19,35,41]. Hereafter we discuss these two sets of works.…”

Section: Related Workmentioning

confidence: 99%

What’s the Gist? Privacy-Preserving Aggregation of User Profiles

Bilogrevic

Freudiger²,

Cristofaro

et al. 2014

Lecture Notes in Computer Science

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Over the past few years, online service providers have started gathering increasing amounts of personal information to build user profiles and monetize them with advertisers and data brokers. Users have little control of what information is processed and are often left with an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model -the "gist" -of their data. We aim to preserve data utility and simultaneously provide user privacy. We show that this approach can be efficiently supported by letting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. We evaluate our framework on a dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it generates revenue for both users and data brokers, and (iii) its overhead is appreciably low.

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PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Cited by 186 publications

References 20 publications

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error

Privacy, Trust and Incentives in Participatory Sensing

What’s the Gist? Privacy-Preserving Aggregation of User Profiles

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