会议信息
PETS 2025: Privacy Enhancing Technologies Symposium
https://petsymposium.org/2025/
截稿日期:
2025-02-28
通知日期:
2025-05-01
会议日期:
2025-07-14
会议地点:
Washington DC, USA
届数:
25
CCF: c   CORE: b   浏览: 35696   关注: 100   参加: 6

征稿
Papers submitted to PETS/PoPETs should present novel practical and/or theoretical research into the requirements, design, analysis, experimentation, or fielding of privacy-enhancing technologies and the social, cultural, legal, or situational contexts in which they are used. PETS is also open to interdisciplinary research examining people’s and communities’ privacy needs, preferences, and expectations as long as it is clear how these findings can impact the design, development, or deployment of technology with privacy implications.

Note that a paper's relevance to privacy applications is crucial. PETS is open to topics from the wider area of security and privacy as long as it is clear how these serve to improve or understand privacy in technology (e.g., it includes a use case, evaluation on real data, integration with an application, etc.) in real systems. A paper that makes significant contributions in an area such as theoretical cryptography but fails to clearly tie its contributions to improving or understanding privacy in real systems is unlikely to be accepted. The chairs and the program committee, at their discretion, may reject papers based on scope

Clarification of expectations: This year we introduced a new requirement that submissions must contribute to real privacy applications that run in real systems. Consistent with this focus, we expect that submissions should not need to rely on proofs as a primary contribution and thus proofs would usually appear in the Appendix rather than in the main body, and a substantial portion of each submission would instead be focused on work that is more traditionally considered practical or applied work (e.g., real-world use cases, real-world measurements, evaluation on real-world data, application development, integration with a real-world application, system design and evaluation, etc.). This focus is necessary due to an increasing number of submissions that make primary contributions that are highly theoretical in nature (e.g., to theoretical cryptography and primitives or related areas) for which PoPETs is not well-equipped to review and provide high quality feedback.

Suggested topics include but are not restricted to:

    Anonymous communication and censorship resistance
    Blockchain privacy
    Building and deploying privacy-enhancing systems
    Cloud computing and privacy
    Compliance with privacy laws and regulations
    Cryptographic tools for privacy
    Data protection technologies
    Defining and quantifying privacy
    Differential privacy and private data analysis
    Economics and game-theoretical approaches to privacy
    Forensics and privacy
    Genomic and medical privacy
    Human factors, usability, and user-centered design of privacy technologies
    Information leakage, data correlation, and abstract attacks on privacy
    Interdisciplinary research connecting privacy to economics, law, psychology, etc.
    Internet of Things privacy
    Location privacy
    Machine learning and privacy
    Measurement of privacy in real-world systems
    Mobile devices and privacy
    Policy languages and tools for privacy
    Profiling and data mining
    Social network privacy
    Surveillance
    Traffic analysis
    Transparency, fairness, robustness, and abuse in privacy systems
    Web privacy

We also solicit Systematization of Knowledge (SoK) papers on any of these topics: papers putting together existing knowledge under some common light (adversary model, requirements, functionality offered, etc.), providing novel insights, identifying research gaps or challenges to commonly held assumptions, etc. Survey papers, without such contributions, are not suitable. SoK submissions should include "SoK:" in their title and check the corresponding option in the submission form.
最后更新 Dou Sun 在 2024-09-16
录取率
时间提交数录取数录取率(%)
2014861618.6%
2013691318.8%
2012721622.2%
2011611524.6%
2010571628.1%
2009441431.8%
2008481327.1%
2007841619%
2006912426.4%
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