Open AccessProceedings Article
Privacy-preserving SVM classification
Jaideep Vaidya,Hwanjo Yu,Xiaoqian Jiang +2 more
- Vol. 14, Iss: 2, pp 161-178
TLDR
In this article, a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short, is proposed, which constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others.Abstract:
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges.read more
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Posted Content
Advances and Open Problems in Federated Learning
Peter Kairouz,H. Brendan McMahan,Brendan Avent,Aurélien Bellet,Mehdi Bennis,Arjun Nitin Bhagoji,Kallista Bonawitz,Zachary Charles,Graham Cormode,Rachel Cummings,Rafael G. L. D'Oliveira,Hubert Eichner,Salim El Rouayheb,David Evans,Josh Gardner,Zachary Garrett,Adrià Gascón,Badih Ghazi,Phillip B. Gibbons,Marco Gruteser,Zaid Harchaoui,Chaoyang He,Lie He,Zhouyuan Huo,Ben Hutchinson,Justin Hsu,Martin Jaggi,Tara Javidi,Gauri Joshi,Mikhail Khodak,Jakub Konečný,Aleksandra Korolova,Farinaz Koushanfar,Sanmi Koyejo,Tancrède Lepoint,Yang Liu,Prateek Mittal,Mehryar Mohri,Richard Nock,Ayfer Ozgur,Rasmus Pagh,Mariana Raykova,Hang Qi,Daniel Ramage,Ramesh Raskar,Dawn Song,Weikang Song,Sebastian U. Stich,Ziteng Sun,Ananda Theertha Suresh,Florian Tramèr,Praneeth Vepakomma,Jianyu Wang,Li Xiong,Zheng Xu,Qiang Yang,Felix X. Yu,Han Yu,Sen Zhao +58 more
TL;DR: Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
Journal Article
Privacy preserving association rule mining in vertically partitioned data
TL;DR: A privacy preserving association rule mining algorithm was introduced that preserved privacy of individual values by computing scalar product and the security was analyzed.
Journal ArticleDOI
Machine learning on big data
TL;DR: A framework of ML on big data (MLBiD) is introduced to guide the discussion of its opportunities and challenges and provides directions for identification of associated opportunities and challenged and open up future work in many unexplored or under explored research areas.
Posted Content
SecureML: A System for Scalable Privacy-Preserving Machine Learning.
Payman Mohassel,Yupeng Zhang +1 more
TL;DR: In this article, the authors present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method, where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation.
Journal ArticleDOI
Information Security in Big Data: Privacy and Data Mining
TL;DR: This paper identifies four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker, and examines various approaches that can help to protect sensitive information.
References
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Book
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Proceedings ArticleDOI
How to play ANY mental game
TL;DR: This work presents a polynomial-time algorithm that, given as a input the description of a game with incomplete information and any number of players, produces a protocol for playing the game that leaks no partial information, provided the majority of the players is honest.
Journal ArticleDOI
Privacy-preserving data mining
TL;DR: This work considers the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed and proposes a novel reconstruction procedure to accurately estimate the distribution of original data values.
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