Differentially Private Set Union
Jul 12, 2020
Sprecher:innen
Sivakanth Gopi
Řečník · 0 sledujících
Pankaj Gulhane
Řečník · 0 sledujících
Janardhan Kulkarni
Řečník · 0 sledujících
Judy Hanwen Shen
Řečník · 0 sledujících
Milad Shokouhi
Řečník · 0 sledujících
Sergey Yekhanin
Řečník · 0 sledujících
Über
We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe U of items, possibly of infinite size, and a database D of users. Each user i contributes a subset W_i ⊆ U of items. We want an (ϵ,δ)-differentially private Algorithm which outputs a subset S ⊂∪_i W_i such that the size of S is as large as possible. The problem arises in countless real world applications, and is particularly important and ubiquitous in natural language processing (NLP) problems. For example, discovering words, sentences, n-grams etc., from private text data belonging to users is an instance of the set union problem. Known algorithms for this problem proceed by collecting a (weighted) subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain cutoff threshold. Crucially, in the above process, the contribution of each individual user is always independent from identity of items held by other users, resulting in a wasteful aggregation process, where some items’ counts happen to be very large – far above the cutoff threshold. We deviate from the above paradigm, by allowing users to contribute their items in a dependent fashion, guided by a policy. In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain contractive properties would result in a differentially private algorithm. We design two new algorithms, one using Laplace Noise and other Gaussian noise, as specific instances of policies satisfying the contractive properties. Our experiments show that the new algorithms significantly outperform previously known mechanisms for the problem.We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe U of items, possibly of infinite size, and a database D of users. Each user i contributes a subset W_i ⊆ U of items. We want an (ϵ,δ)-differentially private Algorithm which outputs a subset S ⊂∪_i W_i such that the size of S is as large as possible. The problem arises in countless real world applications, and is particularly important and ubiquitous in natural language…
Kategorien
Vývoj webu a UX/UI
Kategorie · 1,2k prezentací
Umělá inteligence a data science
Kategorie · 10,8k prezentací
Vývoj aplikací a her
Kategorie · 954 prezentací
Über ICML 2020
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
Gefällt euch das Format? Vertraut auf SlidesLive, um euer nächstes Event festzuhalten!
Professionelle Aufzeichnung und Livestreaming – weltweit.
Freigeben
Empfohlene Videos
Präsentationen, deren Thema, Kategorie oder Sprecher:in ähnlich sind
Poster #48
Zhlédnout později
Deep Reinforcement Learning with Smooth Policy
Zhlédnout později
Qianli Shen, …
Endless Frontiers?
Zhlédnout později
On Learning Sets of Symmetric Elements
Zhlédnout později
Haggai Maron, …
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Zhlédnout později
