By Bhavani Thuraisingham (auth.), Francesco Bonchi, Elena Ferrari, Wei Jiang, Bradley Malin (eds.)
ISBN-10: 3642017177
ISBN-13: 9783642017179
ISBN-10: 3642017185
ISBN-13: 9783642017186
This e-book constitutes the completely refereed post-workshop complaints of the second one foreign Workshop on privateness, safeguard, and belief in KDD, PinKDD 2008, held in Las Vegas, NV, united states, in March 2008 along with the 14th ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2008.
The five revised complete papers awarded including 1 invited keynote lecture and a couple of invited panel periods have been conscientiously reviewed and chosen from quite a few submissions. The papers are prolonged models of the workshop shows and contain reviewers' reviews and discussions on the workshop and signify the range of knowledge mining study matters in privateness, protection, and belief in addition to present paintings on privateness matters in geographic facts mining.
Read or Download Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, NV, USA, August 24, 2008, Revised Selected Papers PDF
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Extra info for Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, NV, USA, August 24, 2008, Revised Selected Papers
Sample text
The formal definition of a masked social network that is k-anonymous is presented below. Definition 3. (k-anonymous masked social network): A masked social network MG = (MN , ME), where MN = {Cl1 , Cl2 , . . , Clv }, and Clj = [gen(clj ), (|clj |, |Eclj |)], j = 1, . . , v is k -anonymous iff |clj | ≥ k for all j = 1, . . , v. 3 The SaNGreeA Algorithm The algorithm described in this section, called the SaNGreeA (Social Network Greedy Anonymization) algorithm, performs a greedy clustering processing to generate a k-anonymous masked social network, given an initial social network modeled as a graph G = (N , E).
E. the task of finding items that are frequently purchased together, based on point-of-sale data collected at cash registers. In cluster analysis, the goal is to partition a data set into groups of closely related data in such a way that the observations belonging to the same group, or cluster, are similar to each other, while the observations belonging to different clusters are not. Clustering can be used, for instance, to find segments of customers with a similar purchasing behavior or categories of documents pertaining to related topics.
Consequently, a first criterion to lead the clustering process is to ensure that each cluster has enough elements. As it is well-known, (attribute and relationship) generalization results in information loss. Therefore, a second criterion used during clustering is to minimize the information lost between the initial social network data and its masked version, caused by the subsequent cluster-level quasi-identifier attributes and relationship generalization. In order to obtain good quality masked data, and also to permit the user to control the type and the quantity of information loss he/she can afford, the clustering algorithm uses two information loss measures.
Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, NV, USA, August 24, 2008, Revised Selected Papers by Bhavani Thuraisingham (auth.), Francesco Bonchi, Elena Ferrari, Wei Jiang, Bradley Malin (eds.)
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