Conference Information

PETS 2027: Privacy Enhancing Technologies Symposium

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PETS
Submission Date:
2027-02-28 Due in 219 days
Notification Date:
2027-05-01
Conference Date:
2027-07-19
Location:
Delft, The Netherlands
Years:
CCF: C   ICORE: A   Viewed: 102367   Tracked: 104   Attend: 7

Call For Papers

PETS 2027 (Privacy Enhancing Technologies Symposium) is a CCF C / ICORE A conference held in Delft, The Netherlands on 2027-07-19. The paper submission deadline is 2027-02-28. Acceptance notifications are sent on 2027-05-01.

Scope (Refined for PoPETs 2027) Papers must present novel research into privacy-enhancing technologies (PETs) and the social, cultural, legal, or situational contexts in which they are used. Interdisciplinary work is welcome as long as it clearly shows how the contributions impact PETs. 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. Papers must have strong ties to privacy in digital systems. The core contribution must be relevant to real-world privacy applications. The first page must clearly state how the work is relevant to real-world privacy applications. Relevance to real-world privacy applications must also be present throughout the entire paper by dedicating a substantial portion of the submission to work that is traditionally considered practical or applied. For contributions of theoretical nature (e.g., improvements on cryptographic primitives or improvements on differential privacy), the paper should provide details about how the proposed advances would be integrated into a real-world application. Improvements motivated by real applications, but presented in isolation without connection to those applications, do not demonstrate relevance in practice and are therefore out of scope. Papers with empirical evaluations must show a clear connection to privacy and to real-world applications. Evaluations that are based on purely synthetic datasets, or on non-privacy-relevant datasets (e.g., MNIST, CIFAR, or SVHN) must contain a well-marked paragraph explaining why the results of the evaluation can be extrapolated to real-world use cases where privacy is of relevance. Papers that treat privacy as a superficial application domain (e.g., if the only relationship to privacy is a privacy-related dataset) do not demonstrate relevance to real-world privacy problems and are therefore out of scope. 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.
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Acceptance Ratio

Average acceptance rate: 24.1% over 9 years (2006–2014).

YearSubmittedAcceptedAccepted(%)
2014861618.6%
2013691318.8%
2012721622.2%
2011611524.6%
2010571628.1%
2009441431.8%
2008481327.1%
2007841619%
2006912426.4%

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