Book ChapterDOI
I have a DREAM!: differentially private smart metering
Gergely Acs,Claude Castelluccia +1 more
- pp 118-132
TLDR
This paper presents a new privacy-preserving smart metering system that is private under the differential privacy model and therefore provides strong and provable guarantees.Abstract:
This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees.With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics without learning anything about the activities of individual households. For example, a supplier cannot tell from a user's trace whether or when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.read more
Citations
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Proceedings ArticleDOI
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: In this paper, the authors proposed a secure aggregation of high-dimensional data for federated deep neural networks, which allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner without learning each user's individual contribution.
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 ArticleDOI
Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams
TL;DR: This paper has implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions.
Posted Content
Practical Secure Aggregation for Privacy Preserving Machine Learning.
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: This protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner, and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network.
Proceedings ArticleDOI
A Hybrid Approach to Privacy-Preserving Federated Learning
TL;DR: This paper presents an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs and enables the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust.
References
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Journal ArticleDOI
New Directions in Cryptography
TL;DR: This paper suggests ways to solve currently open problems in cryptography, and discusses how the theories of communication and computation are beginning to provide the tools to solve cryptographic problems of long standing.
Book ChapterDOI
Calibrating noise to sensitivity in private data analysis
TL;DR: In this article, the authors show that for several particular applications substantially less noise is needed than was previously understood to be the case, and also show the separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Journal Article
Calibrating noise to sensitivity in private data analysis
TL;DR: The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.
Journal ArticleDOI
Nonintrusive appliance load monitoring
TL;DR: In this paper, a nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described.
Book ChapterDOI
Our data, ourselves: privacy via distributed noise generation
TL;DR: In this paper, a distributed protocol for generating shares of random noise, secure against malicious participants, was proposed, where the purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers.