Distributed Privacy Preserving Information Sharing

Privacy-Preserving Distributed Information Sharing

In many important applications, a collection of mutually distrustful parties must share information, without compromising their privacy. Currently, these applications are often performed by using some form of a trusted third party (TTP); this TTP receives all players’ inputs, computes the desired function, and returns the result. However, the level of trust that must be placed in such a TTP is ...

A review on Security in Distributed Information Sharing

In recent year’s privacy preserving data mining has emerged as a very active research area in data mining. Over the last few years this has naturally lead to a growing interest in security or privacy issues in data mining. More precisely, it became clear that discovering knowledge through a combination of different databases raises important security issues. Privacy preserving data mining is on...

On Effective Protection of Security and Privacy in XML Information Brokering

In contrast with the situations when the information seeker knows where the needed data is located, XML Information Brokering System (IBS) needs to help each information seeking query ”locate” the corresponding data source(s). Unlike early information sharing approaches that only involve a small number of databases, new information sharing applications are often assumed to be built atop a large...

Thesis Proposal: Privacy Preserving Distributed Information Sharing

An Efficient Approach for Privacy Preserving Distributed K-Means Clustering Based on Shamir's Secret Sharing Scheme

Privacy preserving data mining has gained considerable attention because of the increased concerns to ensure privacy of sensitive information. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to ex...

Privacy-Preserving Distributed Data Mining Techniques: A Survey

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 ...