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Found 8 papers, 3 papers with code
Closed-Form Bounds for DP-SGD against Record-level Inference
This paper presents a new approach to evaluate the privacy of machine learning models against specific record-level threats, such as membership and attribute inference, without the indirection through DP.
Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective
In this paper, we take an information flow control perspective to describe machine learning systems, which allows us to leverage metadata such as access control policies and define clear-cut privacy and confidentiality guarantees with interpretable information flows.
Analyzing Leakage of Personally Identifiable Information in Language Models
Understanding the risk of LMs leaking Personally Identifiable Information (PII) has received less attention, which can be attributed to the false assumption that dataset curation techniques such as scrubbing are sufficient to prevent PII leakage.
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data.
Bayesian Estimation of Differential Privacy
Our Bayesian method exploits the hypothesis testing interpretation of differential privacy to obtain a posterior for $\varepsilon$ (not just a confidence interval) from the joint posterior of the false positive and false negative rates of membership inference attacks.
Analyzing Information Leakage of Updates to Natural Language Models
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.
Analyzing Privacy Loss in Updates of Natural Language Models
To continuously improve quality and reflect changes in data, machine learning-based services have to regularly re-train and update their core models.
Verified Low-Level Programming Embedded in F*
Low* is a shallow embedding of a small, sequential, well-behaved subset of C in F*, a dependently-typed variant of ML aimed at program verification.
Programming Languages Cryptography and Security
