The 1st International Workshop on Privacy Algorithms in Systems (PAS)
PAS Workshop at CIKM'22
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About

Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we propose the PAS at CIKM’22, which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.

PAS at CIKM'22 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of privacy algorithms in systems.


Topics of Interest

Recently privacy has been widely used in many scientific domains and everyday communications and systems. Even privacy regulations have been developed on a national and international level. Privacy topic affects all kinds of complex datasets such as text, video, audio, image, streaming, and graph format. They are produced by surveillance systems, home management systems, user tracking devices, data storage, communication systems, social networks, etc. Machine learning/decision making algorithmic systems have been widely adopted to facilitate efficient systems. As our ability to generate and collect data constantly increases unprecedentedly, the complex data we are facing in the modern era are becoming more and more diverse and large-scale. This raises privacy concerns for users, systems, and infrastructure, leading to more efforts to develop effective privacy preservation algorithms and deploy them efficiently for real-world applications.

This workshop aims to discuss the recent research progress of privacy algorithms in both theoretical foundations and practical applications for systems. We invite submissions that focus on recent advances in research/development of privacy algorithms and their applications. Theory and methodology papers are welcome from any of the following areas, including but not limited to:
  • Theory of privacy algorithms (e.g., differential privacy, local differential privacy, pan-privacy, data anonymization)
  • Privacy preservation of complex data (e.g., image, text, video, audio, streaming data, graph data) and data sharing
  • Privacy preservation decision making algorithms, machine and federated learning, transfer and semi-supervised learning, meta-learning
  • Privacy preservation in deep learning models (e.g., convolutional neural networks, graph neural networks, recurrent neural networks, transformer-based networks, etc.)
  • Benchmark analysis of privacy algorithms
  • Relation between privacy guarantee, fairness, and bias
  • Metrics for data privacy
  • Implementation of privacy policies and regulations in privacy algorithms
  • Privacy attacks on complex data and methods
and application papers focused on but not limited to:
  • Recommender Systems, Computer Vision, Natural Language Processing
  • Biomedical, Healthcare, Insurance
  • Cybersecurity, Financial security, Consumer protection
  • Transportation/Mobility networks
  • Cloud, Edge, and HPC systems


Important Dates

  • Submission deadline: August 29th, 2022
  • Notification of Acceptance: September, 15th, 2022
  • Camera-ready paper due: Septebmer, 30th, 2022
  • PAS at CIKM'22 Workshop day: October 21th, 2022

Submission Details

All submissions must be in PDF format and formatted according to the new ACM format published in ACM guidelines (e.g., using the ACM LaTeX template on Overleaf here) and selecting the "sigconf" sample. Following the WSDM conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted works will be assessed based on their novelty, technical quality, potential impact, and clarity of writing (and should be in English). For papers that primarily rely on empirical evaluations, the experimental settings and results should be clearly presented and repeatable. We encourage authors to make data and code available publicly when possible. Accepted papers will be posted on this workshop website and will not appear in the CIKM proceedings and are thus non-archival (allowing you to submit works to PAS at CIKM'22 even if they are current under review elsewhere). The best paper (according to the reviewers' ratings) will be announced at the end of the workshop.

All submissions must be uploaded electronically to EasyChair at: Submission Page

At least one of the authors of the accepted workshop papers must register for the workshop and be present on the day of the workshop.

For questions regarding submissions, please contact us at: cikm2022pas@easychair.org
Submissions can fall in one of the following categories:
  • Long research papers (5-10 pages)
  • Short research/application papers (2-4 pages)

Workshop Program

The workshop will be hybrid where at least two of the organizers are going to attend the workshop in person to ensure everything runs smooth. The workshop will be advertised to our local institutions, our collaborator networks, relevant universities, industry, and on social platforms. The full day workshop schedule is planned with two half-day sessions that are split by the conference planned lunch break (which we observed historically to be 12:30 - 13:30 in the past but will update accordingly as needed).
Our program will consist of the following main components:
  • invited keynotes from experts in the field of privacy algorithms coming from both industry and academia to create a synergistic atmosphere and to stimulate collaborations.
  • contributed research oral talks selected from the set of accepted works into PAS.
  • future directions panel discussion that will be composed of our keynote speakers given their expertise in this domain.
  • contributed poster sessions both before lunch and after the final remarks to allow all those with works accepted into the workshop (not just those selected for oral talks) to present their work and socialize stimulating new ideas and potential collaborations.
We will post detailed program if our workshop proposal were to be accepted.

Confirmed Keynote Speakers

Graham Cormode

Professor

University of Warwick and Meta (UK)

Norman Sadeh

Professor

Carnegie Mellon University (USA)

James Joshi

Professor

University of Pittsburgh (USA)

Dan Lin

Associate Professor

University of Missouri (USA)

James Honaker

Scientist

Harvard University and Meta (USA)

Harsha Nori

Senior Data Scientist

Microsoft (USA)

Organization


Workshop Co-Chairs

Philip S. Yu

Distinguished Professor

University of Illinois Chicago

Olivera Kotevska

Research Scientist

Oak Ridge National Laboratory

Tyler Derr

Assistant Professor

Vanderbilt University




Additional Workshop Organizers

Proceedings Chair


Chris Stanley

Research Scientist

Oak Ridge National Laboratory

Web Chair


Yuying Zhao

PhD Student

Vanderbilt University