90 citations · 331 across the 28 of their papers we have counts for
15 papers · 1 filter
Permissive Information-Flow Analysis for Large Language Models
Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf +7
Large Language Models (LLMs) are rapidly becoming commodity components of larger software systems. This poses natural security and privacy problems: poisoned data retrieved from on…
Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective
Lukas Wutschitz, Boris Köpf, Andrew Paverd +6
Modern machine learning systems use models trained on ever-growing corpora. Typically, metadata such as ownership, access control, or licensing information is ignored during traini…
Why Train More? Effective and Efficient Membership Inference via Memorization
Jihye Choi, Shruti Tople, Varun Chandrasekaran +1
Membership Inference Attacks (MIAs) aim to identify specific data samples within the private training dataset of machine learning models, leading to serious privacy violations and…
Analyzing Leakage of Personally Identifiable Information in Language Models
Nils Lukas, Ahmed Salem, Robert Sim +3
Language Models (LMs) have been shown to leak information about training data through sentence-level membership inference and reconstruction attacks. Understanding the risk of LMs…
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans +5
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of in…
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth +2
Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are t…
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo +1
Federated learning enables training high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vul…
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople +6
Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees. However, there is a discrepancy between the protection that su…
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Divyat Mahajan, Shruti Tople, Amit Sharma
With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn "stable" feature representations whose effect on the output remai…
Causally Constrained Data Synthesis for Private Data Release
Varun Chandrasekaran, Darren Edge, Somesh Jha +3
Making evidence based decisions requires data. However for real-world applications, the privacy of data is critical. Using synthetic data which reflects certain statistical propert…
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label. We show that this objective…
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that…
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers
Bijeeta Pal, Shruti Tople
Transfer learning --- transferring learned knowledge --- has brought a paradigm shift in the way models are trained. The lucrative benefits of improved accuracy and reduced trainin…
Analyzing Information Leakage of Updates to Natural Language Models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople +5
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models. We show that a differential analy…
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya Nori
Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a d…