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

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)