383 research outputs found
Online and Differentially-Private Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regarding
robustness, memory efficiency, and differential privacy of tensor
decomposition. We propose simple variants of the tensor power method which
enjoy these strong properties. We present the first guarantees for online
tensor power method which has a linear memory requirement. Moreover, we present
a noise calibrated tensor power method with efficient privacy guarantees. At
the heart of all these guarantees lies a careful perturbation analysis derived
in this paper which improves up on the existing results significantly.Comment: 19 pages, 9 figures. To appear at the 30th Annual Conference on
Advances in Neural Information Processing Systems (NIPS 2016), to be held at
Barcelona, Spain. Fix small typos in proofs of Lemmas C.5 and C.
Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism
Building a recommendation system involves analyzing user data, which can
potentially leak sensitive information about users. Anonymizing user data is
often not sufficient for preserving user privacy. Motivated by this, we propose
a privacy-preserving recommendation system based on the differential privacy
framework and matrix factorization, which is one of the most popular algorithms
for recommendation systems. As differential privacy is a powerful and robust
mathematical framework for designing privacy-preserving machine learning
algorithms, it is possible to prevent adversaries from extracting sensitive
user information even if the adversary possesses their publicly available
(auxiliary) information. We implement differential privacy via the Gaussian
mechanism in the form of output perturbation and release user profiles that
satisfy privacy definitions. We employ R\'enyi Differential Privacy for a tight
characterization of the overall privacy loss. We perform extensive experiments
on real data to demonstrate that our proposed algorithm can offer excellent
utility for some parameter choices, while guaranteeing strict privacy.Comment: 30 page
Improved differential privacy for SGD via optimal private linear operators on adaptive streams
CCF-1763786 - National Science Foundation; Apple, Inchttps://arxiv.org/abs/2202.0831
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