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Privacy preserving data mining for n...

Liu, Lian.

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Privacy preserving data mining for numerical matrices, social networks, and big data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Privacy preserving data mining for numerical matrices, social networks, and big data./
作者:	Liu, Lian.
面頁冊數:	165 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.
Contained By:	Dissertation Abstracts International76-08B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3691877
ISBN:	9781321667851

Privacy preserving data mining for numerical matrices, social networks, and big data.
Liu, Lian.

Privacy preserving data mining for numerical matrices, social networks, and big data. - 165 p.

Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.

Thesis (Ph.D.)--University of Kentucky, 2015.

This item must not be sold to any third party vendors.

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary.

ISBN: 9781321667851Subjects--Topical Terms:

626642
Computer Science.

Privacy preserving data mining for numerical matrices, social networks, and big data.
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245 1 0 $a Privacy preserving data mining for numerical matrices, social networks, and big data.
300 $a 165 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.
500 $a Adviser: Jun Zhang.
502 $a Thesis (Ph.D.)--University of Kentucky, 2015.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary.
520 $a First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated.
520 $a Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks.
520 $a Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements.
520 $a Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound.
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