Abstract— Privacy preservation is the most concentrated issue in data publishing, as the sensitive information should not be leaked. For this sake, several techniques such as generalization, bucketization are proposed, in order to deal with privacy preservation. However, generalization fails on high dimensional data because of dimensionality and it causes information loss due to uniform distrib...

Data privacy preservation has drawn considerable interests in data mining research recently. The k-anonymity model is a simple and practical approach for data privacy preservation. This paper proposes a novel clustering method for conducting the k-anonymity model effectively. In the proposed clustering method, feature weights are automatically adjusted so that the information distortion can be ...

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...

There is now an incredible wealth of data about individuals, businesses and organizations. This data is freely available over the Internet to almost anyone willing to pay for it, independently of whether they are identity thieves or credit card scam artists or legitimate users. This has led to a growing need for privacy. In this paper, we first present a simple logical model of privacy. We then...

Privacy preservation is major issue in current data transmission over internet and cloud network. For the integrity and security of data various methods are used such as cryptography, data transformation, Steganography, watermarking and many more method. In consequence of all these method some data mining technique is used. The data mining technique provide Varity of algorithm for privacy prese...

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40776-5_25. Abstract. In this paper we analyse the trade-off between privacy-preservation methods and the quality of data mining applications, within the specific context of the smart grid. The use of smart meters to automate data collection is set to solve the problem of electricity theft, which is a serious...

Privacy-preservation is a step in data mining that tries to safeguard sensitive information from unsanctioned disclosure and hence protecting individual data records and their privacy. There are various privacy preservation techniques like k-anonymity, l-diversity and t-closeness and data perturbation. In this paper k-anonymity privacy protection technique is applied to high dimensional dataset...

Recently, data privacy in wireless sensor networks (WSNs) has been paid increased attention. The characteristics of WSNs determine that users' queries are mainly aggregation queries. In this paper, the problem of processing aggregation queries in WSNs with data privacy preservation is investigated. A Ring-based Privacy-Preserving Aggregation Scheme (RiPPAS) is proposed. RiPPAS adopts ring struc...

With decentralized optimization having increased applications in various domains ranging from machine learning, control, to robotics, its privacy is also receiving attention. Existing solutions for achieve by patching information-technology mechanisms such as differential or homomorphic encryption, which either sacrifices accuracy incurs heavy computation/communication overhead. We propose an i...