1st ACM International Workshop on Privacy and Anonymity for Very Large Datasets (PAVLAD 2009) (PAVLAD)

Venue: Hong Kong

Location: Hong Kong, China

Event Date/Time: Nov 06, 2009 End Date/Time: Nov 06, 2009
Early Registration Date: Aug 15, 2009
Paper Submission Date: Jul 13, 2009
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Description

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. Statistical agencies, for instance, are collecting large amounts of personal information that have to be protected before their publication. Different forms of algorithms and techniques have been proposed to tackle this problem in the literature. The growing accessibility to high-capacity storage devices allows to keep more detailed information from many areas. While this enriches the information and conclusions extracted from this data, helping in everyday decision making processes in enterprises and many other organizations, 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, statistics disclosure techniques, privacy in social networks, privacy in graphs, etc) and those in High Performance and Data Management (algorithms and structures for efficient data management, parallelism exploitation, distributed systems, etc).

Topics of interest include everything involving privacy and anonymity issues arising in the design, development and deployment of managing very large datasets. Some examples are the following:

* Efficient Privacy-Enhancing Techniques
* Efficient Statistical Disclosure Control Techniques
* Data Structures for Improving Performance in Privacy Technologies
* Privacy and Anonymity on Very Large Graphs
* Privacy and Anonymity in Social Networks
* Privacy and Anonymity on the Internet
* Privacy and Anonymity in Health and Medical Databases
* Privacy Preserving Data/Text Mining for Large Datasets
* Parallelism Exploitation Techniques for Privacy and Anonymity
* Privacy and Anonymity on Distributed Databases
* Streaming Data Anonymization
* Data sharing via Anonymization

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