TY - GEN
T1 - Privacy and anonymization for very large datasets
AU - Muntés-Mulero, Victor
AU - Nin, Jordi
PY - 2009
Y1 - 2009
N2 - With the increase of available public data sources and the interest for analyzing them, privacy issues are becoming the eye of the storm in many applications. The vast amount of data collected on human beings and organizations as a result of cyberinfrastructure advances, or that collected by statistical agencies, for instance, has made traditional ways of protecting social science data obsolete. This has given rise to different techniques aimed at tackling this problem and at the analysis of limitations in such environments, such as the seminal study by Aggarwal of anonymization techniques and their dependency on data dimensionality. The growing accessibility to high-capacity storage devices allows keeping more detailed information from many areas. While this enriches the information and conclusions extracted from this data, it poses a serious problem for most of the previous work presented up to now regarding privacy, focused on quality and paying little attention to performance aspects. In this workshop, we want to gather researchers in the areas of data privacy and anonymization together with researchers in the area of high performance and very large data volumes management. We seek to collect the most recent advances in data privacy and anonymization (i.e. anonymization techniques, statistic disclosure techniques, privacy in machine learning algorithms, privacy in graphs or social networks, etc) and those in High Performance and Data Management (i.e. algorithms and structures for efficient data management, parallel or distributed systems, etc).
AB - With the increase of available public data sources and the interest for analyzing them, privacy issues are becoming the eye of the storm in many applications. The vast amount of data collected on human beings and organizations as a result of cyberinfrastructure advances, or that collected by statistical agencies, for instance, has made traditional ways of protecting social science data obsolete. This has given rise to different techniques aimed at tackling this problem and at the analysis of limitations in such environments, such as the seminal study by Aggarwal of anonymization techniques and their dependency on data dimensionality. The growing accessibility to high-capacity storage devices allows keeping more detailed information from many areas. While this enriches the information and conclusions extracted from this data, it poses a serious problem for most of the previous work presented up to now regarding privacy, focused on quality and paying little attention to performance aspects. In this workshop, we want to gather researchers in the areas of data privacy and anonymization together with researchers in the area of high performance and very large data volumes management. We seek to collect the most recent advances in data privacy and anonymization (i.e. anonymization techniques, statistic disclosure techniques, privacy in machine learning algorithms, privacy in graphs or social networks, etc) and those in High Performance and Data Management (i.e. algorithms and structures for efficient data management, parallel or distributed systems, etc).
KW - Efficient privacy-enhancing technologies
KW - Efficient statistical disclosure control
KW - Privacy preserving data mining for large data sets
UR - http://www.scopus.com/inward/record.url?scp=74549124826&partnerID=8YFLogxK
U2 - 10.1145/1645953.1646333
DO - 10.1145/1645953.1646333
M3 - Conference contribution
AN - SCOPUS:74549124826
SN - 9781605585123
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2117
EP - 2118
BT - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
T2 - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Y2 - 2 November 2009 through 6 November 2009
ER -