2008 Proceeding- Editors:
- Farshad Fotouhi,
- Li Xiong,
- Traian Marius Truta
It is our great pleasure to welcome you to the First International Workshop on Privacy and Anonymity in the Information Society (PAIS 2008), collocated with 11th International Conference on Extending Database Technology (EDBT 2008). While the ever increasing computational power together with the huge amount of individual data collected daily by various agencies is of great value for our society, they also pose a significant threat to individuals' privacy. As a result legislators for many countries try to regulate the use and the disclosure of confidential information. Data privacy and anonymity have become a mainstream avenue for research. While privacy is a topic discussed everywhere, data anonymity recently established itself as an emerging area of computer science. Its goal is to produce useful computational solutions for releasing data, while providing scientific guarantees that the identities and other sensitive information of the individuals who are the subjects of the data are protected. The PAIS 2008 workshop is the first in its series and its mission is to provide an open yet focused platform for researchers and practitioners from computer science and other fields that are interacting with computer science in the privacy area such as statistics, healthcare informatics, and law to discuss and present current research challenges and advances in data privacy and anonymity research.
The workshop program features 8 papers that cover a variety of topics, including distributed privacy protection, query auditing, k-anonymization and its applications, and micro-aggregation based data anonymization. In addition, the program includes an invited speech by Dr. Josep Domingo-Ferrer, the UNESCO Chair in Data Privacy at Rovira i Virgili University of Tarragona, Catalonia. The title of his talk is: "Location Privacy via Unlinkability: An Alternative to Cloaking and Perturbation". We hope that you find this program interesting and thought-provoking.
Proceeding Downloads
Location privacy via unlinkability: an alternative to cloaking and perturbation
The usual approach to location privacy is to cloak and/or perturb the positions or trajectories of the mobile objects. However, if unlinkability of the various interactions between a mobile object and the service/control system can be afforded and ...
Distributed privacy preserving k-means clustering with additive secret sharing
Recent concerns about privacy issues motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. However, the current techniques for privacy preserving data mining suffer from high ...
A Bayesian approach for on-line max and min auditing
In this paper we consider the on-line max and min query auditing problem: given a private association between fields in a data set, a sequence of max and min queries that have already been posed about the data, their corresponding answers and a new ...
Design of PriServ, a privacy service for DHTs
By decentralizing control, P2P systems provide efficient, scalable data sharing. However, when sharing data for different purposes (e.g., billing, purchase, shipping, etc.), data privacy can be easily violated by untrustworthy peers wich may use data ...
Protecting privacy in recorded conversations
Professionals in the field of speech technology are often constrained by a lack of speech corpora that are important to their research and development activities. These corpora exist within the archives of various businesses and institutions; however, ...
Data utility and privacy protection trade-off in k-anonymisation
K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way that private information contained within it is hidden, yet the anonymised data is still useful in ...
An efficient clustering method for k-anonymization
The k-anonymity model is a privacy-preserving approach that has been extensively studied for the past few years. To minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group ...
Attribute selection in multivariate microaggregation
Microaggregation is one of the most employed microdata protection methods. The idea is to build clusters of at least k original records, and then replace them with the centroid of the cluster. When the number of attributes of the dataset is large, a ...
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
Micro-data protection is a hot topic in the field of Statistical Disclosure Control (SDC), that has gained special interest after the disclosure of 658000 queries by the AOL search engine in August 2006. Many algorithms, methods and properties have been ...
- Proceedings of the 2008 international workshop on Privacy and anonymity in information society