Privacy - Preserving Distributed Computation
نویسنده
چکیده
P4P: A Practical Framework for Privacy-Preserving Distributed Computation
منابع مشابه
P4P: A Practical Framework for Privacy-Preserving Distributed Computation
P4P: A Practical Framework for Privacy-Preserving Distributed Computation
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In various distributed data mining settings, leakage of the real data is not adequate because of privacy issues. To overcome this problem, numerous privacy-preserving distributed data mining practices have been suggested such as protect privacy of their data by perturbing it with a randomization algorithm and using cryptographic techniques. In this paper, we review and provide extensive survey ...
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This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions...
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Often, the information is sensitive or private in nature and these sensitive data when mined violates the privacy of the individuals. Privacy preserving data mining (PPDM) mines the data but intends to preserve the privacy of susceptible data without ever actually seeing it. This paper recaps the important techniques in PPDM like anonymization, perturbation and cryptography. Nowadays, data mini...
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Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving algorithm for this purpose. The proposed algorithm requires a secure solution of s...
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