Generating Artificial Data for Private Deep Learning

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss.Comment: Privacy-Enhancing Artificial Intelligence and Language Technologies, AAAI Spring Symposium Series, 201

Survey: Leakage and Privacy at Inference Time

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malevolent leakage, available defences, followed by the currently available assessment metrics and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research

Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, this is not necessarily free from privacy and robustness vulnerabilities, e.g., via membership, property, and backdoor attacks. This paper investigates whether and to what extent one can use differential Privacy (DP) to protect both privacy and robustness in FL. To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. Our experiments show that both DP variants do d fend against backdoor attacks, albeit with varying levels of protection-utility trade-offs, but anyway more effectively than other robustness defenses. DP also mitigates white-box membership inference attacks in FL, and our work is the first to show it empirically. Neither LDP nor CDP, however, defend against property inference. Overall, our work provides a comprehensive, re-usable measurement methodology to quantify the trade-offs between robustness/privacy and utility in differentially private FL

Privacy in Practice: Private COVID-19 Detection in X-Ray Images

Machine learning (ML) can help fight the COVID-19 pandemic by enabling rapid screening of large volumes of chest X-ray images. To perform such data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 ML models are in part based on small or skewed datasets, are lacking in their privacy guarantees, and do not investigate practical privacy. In this work, we therefore suggest several improvements to address these open gaps. We account for inherent class imbalances in the data and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets than in previous work. Our evaluation is supported by empirically estimating practical privacy leakage through actual attacks. Based on theory, the introduced DP should help limit and mitigate information leakage threats posed by black-box Membership Inference Attacks (MIAs). Our practical privacy analysis is the first to test this hypothesis on the COVID-19 detection task. In addition, we also re-examine the evaluation on the MNIST database. Our results indicate that based on the task-dependent threat from MIAs, DP does not always improve practical privacy, which we show on the COVID-19 task. The results further suggest that with increasing DP guarantees, empirical privacy leakage reaches an early plateau and DP therefore appears to have a limited impact on MIA defense. Our findings identify possibilities for better utility-privacy trade-offs, and we thus believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy