Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Leontiadis, Iraklis; Elkhiyaoui, Kaoutar; Molva, Refik

doi:10.1007/978-3-319-12280-9_20

Cited by 40 publications

(19 citation statements)

References 11 publications

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“…There have been several studies on privacy preserving aggregation such as [7,11,17] whereby all players are considered as honest-but-curious. In [12], authors study a stronger threat model in which the adversary is the aggregator and may tamper with the aggregated result.…”

Section: Related Workmentioning

confidence: 99%

Secure Aggregation Against Malicious Users

Karakoç¹,

Önen

Bilgin

2021

Proceedings of the 26th ACM Symposium on Access Control Models and Technologies

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Secure aggregation protocols allow an aggregator to compute the sum of multiple users' data in a privacy-preserving manner. Existing protocols assume that users from whom the data is collected, are fully trusted on the correctness of their individual inputs. We believe that this assumption is too strong, for example when such protocols are used for federated learning whereby the aggregator receives all users' contributions and aggregate them to train and obtain the joint model. A malicious user contributing with incorrect inputs can generate model poisoning or backdoor injection attacks without being detected. In this paper, we propose the first secure aggregation protocol that considers users as potentially malicious. This new protocol enables the correct computation of the aggregate result, in a privacy preserving manner, only if individual inputs belong to a legitimate interval. To this aim, the solution uses a newly designed oblivious programmable pseudo-random function. We validate our solution as a proof of concept under a federated learning scenario whereby potential backdoor injection attacks exist. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols.

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Section: Related Workmentioning

confidence: 99%

Secure Aggregation Against Malicious Users

Karakoç¹,

Önen

Bilgin

2021

Proceedings of the 26th ACM Symposium on Access Control Models and Technologies

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“…Reference [18] propose a solution with a fully trusted dealer which may not be suitable for real world applications due to increased communication overheads necessary for their static key management scheme. Emerging work in [19] proposes to eliminate the need for key redistribution following a user join or leave as well as the need for fully trusted key dealer thus moving to a more P2P paradigm which is core to our approach too.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Cognitive Privacy for Personal Clouds

Radenkovic

2016

Mobile Information Systems

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This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure.

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“…Joye and Libert [15] proposed a solution to efficiently decrypt the sum based on the idea of splitting the exponent. Leontiadis et al [16] introduced a secure protocol for aggregation of time-series data that is based on Joye's scheme [15] and the requirements for key updates and for the trusted dealer are eliminated. The main idea of it is to introduce a semitrusted collector which plays the role of an intermediary between the users and the aggregator.…”

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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Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Cited by 40 publications

References 11 publications

Secure Aggregation Against Malicious Users

Secure Aggregation Against Malicious Users

Cognitive Privacy for Personal Clouds

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

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