Turning an enemy into an ally: Privacy In Machine Learning (Pri-ML)

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Data has great potential to provide world-changing solutions to pressing problems such as global hunger and climate change. However, sharing data at the scale required to tackle large problems comes with privacy concerns. My long-term goal is to provide methodological foundations that allow us to properly use and share these potentially instrumental data without privacy violations. Recent progress in privacy-preserving machine learning has centered around differential privacy, due to its provability. However, preserving privacy requires injecting noise in the machine learning system, leading to a fundamental trade-off between privacy and accuracy. The trade-off worsens when the algorithm's output is "sensitive" to any changes in its input, as the noise magnitude has to increase to hide that change. In Part A of the research program, I propose novel methods that can effectively limit the sensitivity. The second challenge arises due to the composability of differential privacy: every access to data reduces the privacy guarantee. Differentially private data generation solves this problem by creating a synthetic dataset that is reusable without limit. Unlike existing works assuming certain data distributions or particular downstream tasks, limiting the usefulness of the generated data, I propose a highly flexible nonparametric kernel-distance-based framework to compare the data and synthetic data distributions, in Part B. The third challenge arises due to the requirements for future algorithmic design imposed by regulations such as the General Data Protection Regulation. In Part C, I propose new frameworks that satisfy both differential privacy and other emerging notions, interpretability, causality, and fairness. Our theoretical and quantitative understanding of their interplay will be instrumental in developing practical algorithms that consider these notions simultaneously. There are two important societal impacts of the research program. First, the techniques like those in Part B ensure privacy protection which can promote more data sharing for the public good. Second, as the Netflix documentary Coded bias points out, automatic decision-making systems based on machine learning algorithms have a great potential to adversely affect people's lives. Augmenting those algorithms to be privacy-preserving, fair, and interpretable as suggested in Part C could potentially improve the quality of everyone's lives. The research program will support 13 trainees, who will learn about a broad range of recent advances in machine learning to be the next generation of machine learning experts. As a female primary investigator, my goal is to give more training opportunities to female students for gender equality in the field. Beyond that, I also intend to be inclusive to under-represented groups, e.g., geographically (such as Africa, South America, and Southeast Asia), or ethnically (indigenous people).