6th International Workshop on Privacy Aspects
of Data Mining

held in conjunction with the IEEE International
Conference on Data Mining (ICDM 2007)
on October 28, 2007 in Omaha, Nebraska, United States.

Privacy aspects of data mining have an important impact on many data analysis applications. In particular, privacy has attracted a lot of attention due to the growth of e-commerce, e-business, e-government, and e-health services. In these electronic services, privacy issues arise because many users have concern about how and where their data and information about their activities will be used. Conversely, technology allows service providers to easily track an individual's actions, behaviors, and habits. Given large data collections of person-specific information, providers can mine data to learn patterns, models, and trends that can be used to provide personalized services. The potential benefits of data mining are substantial, but the collection and analysis of sensitive personal data also creates concerns about privacy, data security, and intellectual property rights.

Even though many nations have developed privacy protection laws and regulations to guard against private use of personal information, the existing laws and their conceptual foundations have become outdated because of changes in technology. Thus, in the absence of adequate safeguards, the use of data mining can jeopardize the privacy and autonomy of individuals. Obtaining the potential benefits of data mining with a privacy-aware technology can enable a wider social acceptance of a multitude of new services and applications based on the knowledge discovery process. These services and applications pose new challenges for novel uses of data mining technology. These challenges have captured the attention of many researchers and administrators across a large number of application domains. Despite such efforts there are still many open issues that deserve further investigation.

The aim of the workshop is to bring together researchers and practitioners interested in the privacy aspects of data mining, both by from a technical perspective and from social and legal perspectives. We hope to attract interest across a wide range of possible data mining subareas, including: web mining, medical data mining, spatio-temporal data mining, ubiquitous knowledge discovery, stream data mining, multimedia mining, and obviously, privacy-preserving data mining. Researchers from both academia and industry are invited to submit papers presenting novel research on these topics of interest.

Topics of interest

Topics of interest include but are not limited to:

• Cryptographic tools for privacy-preserving data mining
• Inference and disclosure control for data mining
• Learning algorithms for randomized/perturbed data
• Legal and regulatory frameworks for data mining and privacy
• Privacy and anonymity in e-commerce and user profiling
• Privacy aspects of business processes and enterprise management
• Privacy aspects of geographic, spatial, and temporal data
• Privacy aspects of ubiquitous computing systems
• Privacy enhancement technologies in web environments
• Privacy policy infrastructure, enforcement, and analysis
• Privacy-preserving link and social network analysis
• Privacy-preserving applications for homeland security
• Privacy-preserving data integration
• Privacy protection in fraud and identify theft prevention
• Privacy threats due to data mining
• Biomedical and healthcare data mining research privacy
• Query systems and access control
• Trust management for data mining

Important Dates

• Submission deadline: August 5, 2007, 11:59 PM PDT
• Notification of acceptance: August 13, 2007
• Camera-ready copies due: August 17, 2007
• Workshop day: October 28, 2007

Submission

Paper submissions should be limited to a maximum of 6 pages in the IEEE 2-column format, the same as the camera-ready format (see the IEEE Computer Society Press Proceedings Author Guidelines). All papers will be reviewed by at least 2 program committee members for their technical merit, originality, significance, and relevance to the workshop. The papers must be in English and should be formatted according to the IEEE CS press guidelines. Accepted papers will be published in the proceedings by the IEEE Computer Society Press.

Papers are submitted electronically on the ICDM 07 paper submission page. Follow the link to Workshop/Paper submission and then select "Workshop #6: Privacy Aspects of Data Mining".

We are looking forward to your contributions!

Accepted Papers

1. Privacy-Preserving k-NN for Small and Large Data Sets, Artak Amirbekyan and Vladimir Estivill-Castro
2. Hiding Sensitive Trajectory Patterns, Osman Abul, Maurizio Atzori, Francesco Bonchi, and Fosca Giannotti
3. Simultaneous Pattern and Data Hiding in Unsupervised Learning, Jie Wang, Jun Zhang, Lian Liu, and Dianwei Han
4. Secure Logistic Regression of Horizontally and Vertically Partitioned Databases, Aleksandra Slavkovic, Yuval Nardi, and Matthew Tibbits
5. Private Inference Control For Aggregate Database Queries, Geetha Jagannathan and Rebecca N. Wright
6. Privacy-Preserving Data Mining Applications in the Malicious Model, Murat Kantarcioglu and Onur Kardes
7. A Secure Clustering Algorithm for Distributed Data Streams, Geetha Jagannathan, Krishnan Pillaipakkamnatt and D. Umano

Program

• 8:30-9:15 Keynote - Privacy Preserving Data Mining: Developments and Directions (slides)
  Dr. Bhavani Thuraisingham (University of Texas at Dallas)
• 9:20-10:00 Session I
  • Hiding Sensitive Trajectory Patterns (slides)
    Osman Abul, Maurizio Atzori, Francesco Bonchi, and Fosca Giannotti
  • Simultaneous Pattern and Data Hiding in Unsupervised Learning (slides)
    Jie Wang, Jun Zhang, Lian Liu, and Dianwei Han
• 10:00-10:30 Coffee Break
• 10:30-11:30 Session II
  • Secure Logistic Regression of Horizontally and Vertically Partitioned Databases (slides)
    Aleksandra Slavkovic, Yuval Nardi, and Matthew Tibbits
  • Privacy-Preserving k-NN for Small and Large Data Sets (slides)
    Artak Amirbekyan and Vladimir Estivill-Castro
  • A Secure Clustering Algorithm for Distributed Data Streams (slides)
    Geetha Jagannathan, Krishnan Pillaipakkamnatt and D. Umano
• 11:30-12:10 Session III
  • Private Inference Control For Aggregate Database Queries (slides)
    Geetha Jagannathan and Rebecca N. Wright
  • Privacy-Preserving Data Mining Applications in the Malicious Model (slides)
    Murat Kantarcioglu and Onur Kardes

The workshop will take place in Room B - Elkhorn C.

Chairs

• Stanley Oliveira, Embrapa (CNPTIA), Campinas, SP, Brazil
• Jaideep Vaidya, Rutgers University, NJ, USA
• Rebecca Wright, Rutgers University, NJ, USA

Program Committee

• Justin Brickell, University of Texas at Austin, USA
• Barbara Carminati, University of Insubria, Italy
• Ping Chen, University of Houston-Downtown, USA
• Ricardo Dahab, State University of Campinas, Brazil
• Vladimir Estivill-Castro, Griffith University, Australia
• Csilla Farkas, University of South Carolina, USA
• Hillol Kargupta, University of Maryland Baltimore County, USA
• Helger Lipmaa, University College, London, UK
• Wagner Meira Junior, Federal University of Minas Gerais, Brazil
• Taneli Mielikäinen, Nokia Research Center, Palo Alto, USA
• Kobbi Nissim, Ben-Gurion University, Israel
• Benny Pinkas, University of Haifa, Israel
• Yücel Saygin, Sabanci University, Turkey
• Aleksandra B. Slavkovic, Penn State University, USA
• Adam Smith, Penn State University, USA
• Vassilis Verykios, University of Thessaly, Greece
• Danfeng Yao, Brown University, USA
• Justin Zhan, Carnegie Mellon University, USA