Description
Springer Privacy Security And Trust In Kdd First Acm Sigkdd International Workshop2008 Edition by Francesco Bonchi Elena Ferrari Bradley Malin Yücel Saygin
Vast amounts of data are collected by service providers and system administ- tors and are available in public information systems. Data mining technologies provide an ideal framework to assist in analyzing such collections for computer security and surveillance-related endeavors. For instance system administrators can apply data mining to summarize activity patterns in access logs so that potential malicious incidents can be further investigated. Beyond computer - curity data mining technology supports intelligence gathering and summari- tion for homeland security. For years and most recently fueled by events such as September 11 2001 government agencies have focused on developing and applying data mining technologies to monitor terrorist behaviors in public and private data collections. Theapplicationof data mining to person-speci?cdata raisesseriousconcerns regarding data con?dentiality and citizens' privacy rights. These concerns have led to the adoption of various legislation and policy controls.In 2005 the - ropean Union passed a data-retention directive that requires all telephone and Internetservice providersto store data ontheir consumers for up to two yearsto assist in the prevention of terrorismand organized crime. Similar data-retention regulationproposalsareunderheateddebateintheUnitedStatesCongress. Yet the debate often focuses on ethical or policy aspects of the problem such that resolutions have polarized consequences; e. g. an organization can either share data for data mining purposes or it can not. Fortunately computer scientists and data mining researchers in particular have recognized that technology can beconstructedtosupportlesspolarizedsolutions. Computerscientistsaredev- oping technologies that enable data mining goals without sacri?cing the privacy and security of the individuals to whom the data correspond. Table of contents : Invited Paper.- An Ad Omnia Approach to Defining and Achieving Private Data Analysis.- Contributed Papers.- Phoenix: Privacy Preserving Biclustering on Horizontally Partitioned Data.- Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework.- Probabilistic Anonymity.- Website Privacy Preservation for Query Log Publishing.- Privacy-Preserving Data Mining through Knowledge Model Sharing.- Privacy-Preserving Sharing of Horizontally-Distributed Private Data for Constructing Accurate Classifiers.- Towards Privacy-Preserving Model Selection.- Preserving the Privacy of Sensitive Relationships in Graph Data.