More Like this

1. Projection-Free Bandit Optimization with Privacy Guarantees

   Ene, Alina; Nguyen, Huy L; Vladu, Adrian (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a complex geometry, and access to it is done efficiently only through a linear optimization oracle, hence Euclidean projections are unavailable (e.g. matroid polytope, submodular base polytope). This is the first differentially-private algorithm for projection-free bandit optimization, and in fact our bound matches the best known non-private projection-free algorithm (Garber-Kretzu, AISTATS '20) and the best known private algorithm, even for the weaker setting when projections are available (Smith-Thakurta, NeurIPS '13). 

   more » « less
2. Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization

   Pedramfar, Mohammad; Nadew, Yididiya Y; Quinn, Christopher J; Aggarwal; Vaneet (May 2024, The Twelfth International Conference on Learning Representations)

   This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries. For every problem considered in the non-monotone setting, the proposed algorithms are either the first with proven sub-linear $$\alpha$$-regret bounds or have better $$\alpha$$-regret bounds than the state of the art, where $$\alpha$$ is a corresponding approximation bound in the offline setting. In the monotone setting, the proposed approach gives state-of-the-art sub-linear $$\alpha$$-regret bounds among projection-free algorithms in 7 of the 8 considered cases while matching the result of the remaining case. Additionally, this paper addresses semi-bandit and bandit feedback for adversarial DR-submodular optimization, advancing the understanding of this optimization area. 

   more » « less
3. Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions

   Asi, H; Liu, D; Tian, K (June 2024, https://doi.org/10.48550/arXiv.2406.02789)

   We study the problem of differentially private stochastic convex optimization (DP-SCO) with heavy-tailed gradients, where we assume a kth-moment bound on the Lipschitz constants of sample functions rather than a uniform bound. We propose a new reduction-based approach that enables us to obtain the first optimal rates (up to logarithmic factors) in the heavy-tailed setting, achieving error G2⋅1n√+Gk⋅(d√nϵ)1−1k under (ϵ,δ)-approximate differential privacy, up to a mild $$\textup{polylog}(\frac{1}{\delta})$$ factor, where G22 and Gkk are the 2nd and kth moment bounds on sample Lipschitz constants, nearly-matching a lower bound of [Lowy and Razaviyayn 2023]. We further give a suite of private algorithms in the heavy-tailed setting which improve upon our basic result under additional assumptions, including an optimal algorithm under a known-Lipschitz constant assumption, a near-linear time algorithm for smooth functions, and an optimal linear time algorithm for smooth generalized linear models. 

   more » « less
4. Differentially Private Vertical Federated Clustering

   https://doi.org/10.14778/3583140.3583146  

   Li, Zitao; Wang, Tianhao; Li, Ninghui (February 2023, Proceedings of the VLDB Endowment)

   In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model. To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data. However, it is challenging to ensure that the shared information maintains privacy while learning accurate models. To the best of our knowledge, the algorithm proposed in this paper is the first practical solution for differentially private vertical federatedk-means clustering, where the server can obtain a set of global centers with a provable differential privacy guarantee. Our algorithm assumes an untrusted central server that aggregates differentially private local centers and membership encodings from local data parties. It builds a weighted grid as the synopsis of the global dataset based on the received information. Final centers are generated by running anyk-means algorithm on the weighted grid. Our approach for grid weight estimation uses a novel, light-weight, and differentially private set intersection cardinality estimation algorithm based on the Flajolet-Martin sketch. To improve the estimation accuracy in the setting with more than two data parties, we further propose a refined version of the weights estimation algorithm and a parameter tuning strategy to reduce the finalk-means loss to be close to that in the central private setting. We provide theoretical utility analysis and experimental evaluation results for the cluster centers computed by our algorithm and show that our approach performs better both theoretically and empirically than the two baselines based on existing techniques 

   more » « less
5. Mitigating Bias in Adaptive Data Gathering via Differential Privacy

   Neel, Seth; Roth, Aaron (January 2018, International Conference on Machine Learning (ICML))

   Data that is gathered adaptively --- via bandit algorithms, for example --- exhibits bias. This is true both when gathering simple numeric valued data --- the empirical means kept track of by stochastic bandit algorithms are biased downwards --- and when gathering more complicated data --- running hypothesis tests on complex data gathered via contextual bandit algorithms leads to false discovery. In this paper, we show that this problem is mitigated if the data collection procedure is differentially private. This lets us both bound the bias of simple numeric valued quantities (like the empirical means of stochastic bandit algorithms), and correct the p-values of hypothesis tests run on the adaptively gathered data. Moreover, there exist differentially private bandit algorithms with near optimal regret bounds: we apply existing theorems in the simple stochastic case, and give a new analysis for linear contextual bandits. We complement our theoretical results with experiments validating our theory. 

   more » « less