Collusion-tolerable Privacy-preserving Sum And Product Calculation Without Secure Channel Pdf

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With the pervasiveness and increasing capability of smart devices, mobile crowdsensing has been applied in more and more practical scenarios and provides a more convenient solution with low costs for existing problems.

In this paper, we propose differentially private protocols for Naive Bayes classification over distributed data. Compared with existing works, the privacy and security models in the proposed protocols are stronger: firstly, both the miner and parties can be arbitrarily malicious and can collude with each other to violate the remaining honest parties privacy; secondly, all communication channels between them can be assumed to be insecure. Specifically, we build a guarantee of differential privacy into the cryptographic construction so that the proposed protocols can tolerate collusions and resist eavesdropping attacks which are caused by insecure communication channels. Additionally, the proposed protocols can be implemented at lower computation and communication costs, and some extensions to our protocols e.

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

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

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Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel. Taeho Jung, Xiang-Yang Li, Senior Member.


Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel

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Many miraculous ideas have been proposed to deal withthe privacy-preserving time-series data aggregation problem in perva-sive computing applications, such as mobile cloud computing. The mainchallenge consists in computing the global statistics of individual inputsthat are protected by some confidentiality mechanism. However, thoseworks either suffer from collusive attack or require a time-consuming ini-tialization at every aggregation request. In this paper, we proposed anefficient aggregation protocol which tolerates up to k passive adversarieswho do not try to tamper the computation.

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Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel-IEEE...

Duplicate citations

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Privacy-Preserving Naive Bayes Classification

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  1. Cravuluner

    Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel · Figures, Tables, and Topics from this paper · Figures and Tables.

  2. Paige B.

    Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel. Abstract: Much research has been conducted to.

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