Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets
نویسندگان
چکیده
منابع مشابه
Scalable Privacy-Preserving Data Mining with Asynchronously Partitioned Datasets
In the Näıve Bayes classification problem using a vertically partitioned dataset, the conventional scheme to preserve privacy of each partition uses a secure scalar product and is based on the assumption that the data is synchronised amongst common unique identities. In this paper, we attempt to discard this assumption in order to develop a more efficient and secure scheme to perform classifica...
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In today’s ever-increasingly digital world, the concept of data privacy has become more and more important. Researchers have developed many privacy-preserving technologies, particularly in the area of data mining and data sharing. These technologies can compute exact data mining models from private data without revealing private data, but are generally slow. We therefore present a framework for...
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Micro data is a valuable source of information for research. However, publishing data about individuals for research purposes, without revealing sensitive information, is an important problem. The main objective of privacy preserving data mining algorithms is to obtain accurate results/rules by analyzing the maximum possible amount of data without unintended information disclosure. Data sets fo...
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Vaidya, Jaideep Shrikant. Ph.D., Purdue University, August, 2004. Privacy Preserving Data Mining over Vertically Partitioned Data. Major Professor: Chris Clifton. The goal of data mining is to extract or “mine” knowledge from large amounts of data. However, data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data. Theore...
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ژورنال
عنوان ژورنال: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
سال: 2013
ISSN: 0916-8508,1745-1337
DOI: 10.1587/transfun.e96.a.111