会議情報

PETS 2026: Privacy Enhancing Technologies Symposium

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投稿締切日:
2026-02-28
通知日:
2026-05-01
開催日:
2026-07-20
開催地:
Calgary, Canada
開催回数:
26
CCF: C   ICORE: A   閲覧: 99521   フォロー: 104   参加: 7

論文募集

PETS 2026 (Privacy Enhancing Technologies Symposium) is a CCF C / ICORE A conference held in Calgary, Canada on 2026-07-20. The paper submission deadline is 2026-02-28. Acceptance notifications are sent on 2026-05-01.

Scope Papers submitted to 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. PoPETs 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. Please follow the guidelines given below to ensure that your submission passes desk review and receives a full review by the program committee. You may ask the chairs for clarification of scope before the submission deadline. (1) Privacy enhancing technologies: Submissions must have strong ties to privacy. The paper's relevance to privacy should be strongly motivated, and ties to privacy should be presented throughout the paper. PoPETs is open to topics from the wider area of security and privacy, but authors of submissions must clearly explain how their work serves to improve or understand privacy in technology. (2) Privacy applications in real systems: Submissions must contribute to real privacy applications that run in real systems. Submissions must provide substantial evidence of this contribution, for example, by dedicating a substantial portion of the submission to work that is traditionally considered practical or applied (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.). Special note for theoretical work: Submissions that make primary contributions that are highly theoretical in nature (e.g., to theoretical cryptography and primitives or related areas) are not directly out of scope. But they have a particularly high risk of being desk-rejected if they do not clearly tie their contributions to privacy enhancing technologies and to privacy applications in real systems. This applies in particular to papers that include proofs as a primary contribution (when they are not a primary contribution, proofs should usually appear in the Appendix). Evidence of ties to real systems can come in many forms, but a particularly preferred one is an evaluation of the theoretical contribution in the context of real systems as outlined above. Authors should make a concerted effort to address both points of scope. This focus is necessary because PoPETs is not well-equipped to review and provide high quality feedback to highly theoretical contributions without relation to real applications with privacy implications. 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
最終更新：Dou Sun（）

採択率

平均採択率: 24.1% 9 年間 (2006–2014).

年投稿数採択数採択率(%)
2014861618.6%
2013691318.8%
2012721622.2%
2011611524.6%
2010571628.1%
2009441431.8%
2008481327.1%
2007841619%
2006912426.4%

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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
IEEE Security & Privacy3.0IEEE1540-7993
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CEngineering Applications of Artificial Intelligence8.0Elsevier0952-1976
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790

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