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

Li, Qinghua; Cao, Guohong

doi:10.1007/978-3-642-39077-7_4

Cited by 49 publications

(42 citation statements)

References 27 publications

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“…Nonetheless, this solution calls for a fully trusted dealer that is able to decrypt users' individual data. The authors in [10] presented a solution to tackle the issue of key redistribution after a user joins or leaves. The propounded solution is based on a ring based grouping technique in which users are clustered into disjoint groups, and consequently, whenever a user joins or leaves only a fraction of the users is affected.…”

Section: Dynamic Group Managementmentioning

confidence: 99%

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Leontiadis

Elkhiyaoui

Molva

2014

Lecture Notes in Computer Science

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Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [8] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of timeseries data that is based on the Joye et al.[8] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al.[8] leave and join operations do not trigger a key update at the users.

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Section: Dynamic Group Managementmentioning

confidence: 99%

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Leontiadis

Elkhiyaoui

Molva

2014

Lecture Notes in Computer Science

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“…due to user failures, was first addressed by Chan et al [8], whose solution we briefly describe in Section VII, but incurs a high aggregation error, which impacts the accuracy of the result. In an effort to minimize the accumulated aggregation error, Li and Cao [29] have each user add very small amounts of noise and order the participants in an interleaved ring structure such that only a subset of users have to update their cryptographic key when the number of active participants changes due to leaves or joins. However, the resulting communication cost is higher compared to [8] and therefore creates a trade-off between accuracy and communication cost.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, our scheme does not incur such a trade-off. Additionally, Li and Cao's [29] scheme (and its extension to computing the minimum in [30]) relies on symmetric-key cryptography.…”

Section: Related Workmentioning

confidence: 99%

Revisiting Private Stream Aggregation: Lattice-Based PSA

Becker

Guajardo

Zimmermann

2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-In this age of massive data gathering for purposes of personalization, targeted ads, etc. there is an increased need for technology that allows for data analysis in a privacy-preserving manner. Private Stream Aggregation as introduced by Shi et al. (NDSS 2011) allows for the execution of aggregation operations over privacy-critical data from multiple data sources without placing trust in the aggregator and while maintaining differential privacy guarantees. We propose a generic PSA scheme, LaPS, based on the Learning With Error problem, which allows for a flexible choice of the utilized privacy-preserving mechanism while maintaining post-quantum security. We overcome the limitations of earlier schemes by relaxing previous assumptions in the security model and provide an efficient and compact scheme with high scalability. Our scheme is practical, for a plaintext space of 2 16 and 1000 participants we achieve a performance gain in decryption of roughly 150 times compared to previous results in Shi et al. (NDSS 2011).

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“…Table 1 summarizes our protocol with major related protocols in the literatures. Besides, there are also several works [17][18][19][20][21][22] on privacy-preserving aggregation of time-series data. Some of them leverage the differential privacy [23] in various ways to achieve privacy as well as collusion (or fault) tolerance.…”

Section: Related Workmentioning

confidence: 99%

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Liu

Wang

et al. 2016

International Journal of Distributed Sensor Networks

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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 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.

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

Cited by 49 publications

References 27 publications

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Revisiting Private Stream Aggregation: Lattice-Based PSA

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

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