Filtering for Private Collaborative Benchmarking

Kerschbaum, Florian; Terzidis, Orestis

doi:10.1007/11766155_29

Cited by 32 publications

(25 citation statements)

References 17 publications

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“…So essentially we hide p − T using two random numbers ρ and ρ ′ . [17] used the same hiding technique in Yao's millionaire's protocol, which is shown to be secure when the range of ρ is big enough and one can increase the difficulty of guessing p − T by randomly using many possible distributions for choosing random numbers. It will reveal p = T only when ρ · (p − T ) − ρ ′ = 0, which occurs with an extremely low probability.…”

Section: F Security Analysis 1) Proximity Pre-filteringmentioning

confidence: 99%

Secure friend discovery in mobile social networks

Dong

Dave

Qiu

et al. 2011

2011 Proceedings IEEE INFOCOM

135

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Abstract-Mobile social networks extend social networks in the cyberspace into the real world by allowing mobile users to discover and interact with existing and potential friends who happen to be in their physical vicinity. Despite their promise to enable many exciting applications, serious security and privacy concerns have hindered wide adoption of these networks. To address these concerns, in this paper we develop novel techniques and protocols to compute social proximity between two users to discover potential friends, which is an essential task for mobile social networks. We make three major contributions. First, we identify a range of potential attacks against friend discovery by analyzing real traces. Second, we develop a novel solution for secure proximity estimation, which allows users to identify potential friends by computing social proximity in a privacy-preserving manner. A distinctive feature of our solution is that it provides both privacy and verifiability, which are frequently at odds in secure multiparty computation. Third, we demonstrate the feasibility and effectiveness of our approaches using real implementation on smartphones and show it is efficient in terms of both computation time and power consumption.

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Section: F Security Analysis 1) Proximity Pre-filteringmentioning

confidence: 99%

Secure friend discovery in mobile social networks

Dong

Dave

Qiu

et al. 2011

2011 Proceedings IEEE INFOCOM

135

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“…Maximum computation for two parties was first presented and evaluated in [20] and the following protocol builds upon the successful experiences. The subscriber sends E common (x i ) to the service provider who maintains an encrypted (intermediate) maximum E common (max).…”

Section: Protocolsmentioning

confidence: 99%

“…Filtering outlying values before benchmarking has been considered in [20], where also the initial idea for the comparison (maximum) protocol was presented, but all communication was done in a peer-to-peer model. Although both papers are concerned with benchmarking as an application, they consider different computations than our statistics.…”

Section: Related Workmentioning

confidence: 99%

“…Companies are reluctant to share their business performance data due to the risk of losing a competitive advantage or being embarrassed. Several privacy-preserving protocols that can be used for benchmarking that keep the KPIs confidential within one company have been proposed in the literature [2,5,9,10,13,20]. None of those matches the requirements of a large service provider offering a benchmarking service entirely.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, not only linear performance is required, but also low constant factors. We know from [20] that a two-party variation of a sub-protocol has superb practical performance. Therefore we are willing to accept the multiplicative hiding assumption (Section 5.1.2) resulting in a significantly improved performance.…”

Section: Introductionmentioning

confidence: 99%

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Practical Privacy-Preserving Benchmarking

Kerschbaum¹

IFIP – The International Federation for Information Processing

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Benchmarking is an important process for companies to stay competitive in today's markets. The basis for benchmarking are statistics of performance measures of a group of companies. The companies need to collaborate in order to compute these statistics.Protocols for privately computing statistics have been proposed in the literature. This paper designs, implements and evaluates a privacy-preserving benchmarking platform which is a central entity that offers a database of benchmark statistics to its customers. This is the first attempt at building a practical privacy-preserving benchmarking system and the first attempt at addressing all necessary trade-offs.The paper starts by designing a protocol that efficiently computes the statistics with constant cost per participant. The protocol uses central communication where customers only communicate with the central platform which facilitates a simple practical orchestration of the protocol. The protocols scale to realistic problem sizes due to the constant communication (and computation) cost per participant of the protocol.

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