Privacy and anonymization for very large datasets

Victor Muntés-Mulero, Jordi Nin

Research output: Book chapterConference contributionpeer-review

26 Citations (Scopus)


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).

Original languageEnglish
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Number of pages2
Publication statusPublished - 2009
Externally publishedYes
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2 Nov 20096 Nov 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CityHong Kong


  • Efficient privacy-enhancing technologies
  • Efficient statistical disclosure control
  • Privacy preserving data mining for large data sets


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