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Protect-ITÌýfocuses on the security and privacy of machine learning (ML) from an information-theoretic standpoint. ML systems rely on extensive datasets, often containing sensitive personal information, posing a threat to user privacy and security.

This workshop aims to bring together experts in machine learning, (algorithmic) fairness and information theory to explore, develop, and evaluate privacy, security, and fairness attacks against ML algorithms along with defense strategies to counter them. We welcome submission on the following topics:

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– Information leakage, data correlation

– Defining and quantifying privacy

– Differential privacy and private data analysis

– Machine learning and privacy

– Transparency, robustness and abuse in privacy systems

– Information theoretic foundations of security and privacy

– Cryptographic tools for privacy

– Algorithmic fairness

– Adversarial learning

– Intrusion detection

– Privacy and security attacks and defenses

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We will have at least one invited speaker to deliver the keynote. The workshop is planned as a full day event.

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SUBMISSION INSTRUCTIONS

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We welcome two types of submissions: full papers of maximum 12 pages or extended abstracts up to 4 pages. Full papers must be unpublished elsewhere and will be published inÌýACM digital libraryÌýas workshop proceedings. We encourage submissions of work that is new to the community of data privacy, security and information theory. Extended abstracts can be previously published or currently under review elsewhere and willÌýnot be includedÌýin proceedings. All submissions must adhere the latest ACM Sigconf style conference template. We also welcome recently published studies as well as unpublished recent results in privacy and security venues only as poster submissions.

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POSTER INSTRUCTIONSÌý

To encourage one-to-one exchanges, in addition to the accepted poster submissions of recent results, all accepted full paper submissions will have a slot at the poster session for posters with size no bigger than A0 (841 × 1189 mm).

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Submission deadline: February 21, 2025 23:59 (Anywhere on Earth)

Notification of acceptance: April 8, 2025

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More information may be found atÌý