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Improving Utility and Security of the Shuffler-based Differential Privacy [article]

Tianhao Wang, Bolin Ding, Min Xu, Zhicong Huang, Cheng Hong, Jingren Zhou, Ninghui Li, Somesh Jha
2020 arXiv   pre-print
Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task.  ...  When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator.  ...  In summary, we improve both the utility and the security aspects of the shuffler model.  ... 
arXiv:1908.11515v3 fatcat:z2hey2wmmrduxfzdkaw7w7jczy

Prochlo

Andrea Bittau, Bernhard Seefeld, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes
2017 Proceedings of the 26th Symposium on Operating Systems Principles - SOSP '17  
The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)  ...  This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy.  ...  Acknowledgments This paper is dedicated to the memory of Andrea Bittau, our colleague who wrote much of PROCHLO. We thank Kunal Talwar for his help analyzing Stash Shuffle's security properties.  ... 
doi:10.1145/3132747.3132769 dblp:conf/sosp/BittauEMMRLRKTS17 fatcat:s5icmqnn6jgznjqhh4fo4fzahm

Learning With Differential Privacy [article]

Poushali Sengupta, Sudipta Paul, Subhankar Mishra
2020 arXiv   pre-print
The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility  ...  The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail.  ...  of differential privacy. • it is a fast framework that provides strong privacy with great utility.  ... 
arXiv:2006.05609v2 fatcat:n4jjarymtvh6toedskl26f66fi

FLAME: Differentially Private Federated Learning in the Shuffle Model [article]

Ruixuan Liu, Yang Cao, Hong Chen, Ruoyang Guo, Masatoshi Yoshikawa
2021 arXiv   pre-print
To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the curator model or local model of differential privacy.  ...  In this work, by leveraging the privacy amplification effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i.e., accuracy in the curator model and strong  ...  Acknowledgements This work is supported by the National Natural Science Foundation of China (No. 62072460, No. 61532021, 61772537, 61772536, 61702522)  ... 
arXiv:2009.08063v4 fatcat:efo6ygwda5amxgxea73vo7hsey

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation [article]

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta
2020 arXiv   pre-print
We also demonstrate how the ESA notion of fragmentation (reporting data aspects in separate, unlinkable messages) improves privacy/utility tradeoffs both in terms of local and central differential-privacy  ...  Based on the Encode-Shuffle-Analyze (ESA) framework, notable results formally clarified large improvements in privacy guarantees without loss of utility by making reports anonymous.  ...  However, the utility improvement of Crowd IDs must come at a cost to privacy.  ... 
arXiv:2001.03618v1 fatcat:r7trb5yhejeb5g7d57lcw2lwya

Privacy Enhancement via Dummy Points in the Shuffle Model [article]

Xiaochen Li, Weiran Liu, Hanwen Feng, Kunzhe Huang, Jinfei Liu, Kui Ren, Zhan Qin
2021 arXiv   pre-print
The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization.In the shuffle model, a shuffler is utilized  ...  The core of DUMP is a new concept of dummy blanket, which enables enhancing privacy by just introducing points on the user side and further improving the utility of the shuffle model.We instantiate DUMP  ...  Or, less "noise" shall be added by users to messages for the same privacy, which in turn improves the utility.  ... 
arXiv:2009.13738v2 fatcat:tdysrn2nxjbjjkkbgcbyn2vbwm

Fairly Private Through Group Tagging and Relation Impact [article]

Poushali Sengupta, Subhankar Mishra
2021 arXiv   pre-print
Lastly, the mechanism boosts the utility through risk minimization function and obtain the optimal privacy-utility budget of the system.  ...  privacy-utility trade-off for both the numerical and decision making Queries.  ...  Privacy at Scale [16] 2020 Privatization, ingestion and aggregation ESA Revisited [35] 2020 Improved [12] with longitudinal privacy BUDS [17] 2020 Optimize the privacy-utility trade-off by Iterative  ... 
arXiv:2105.07244v1 fatcat:utxgfxtuprho7hk6yhojdywnaq

Research Challenges in Designing Differentially Private Text Generation Mechanisms

Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier
2021 Proceedings of the ... International Florida Artificial Intelligence Research Society Conference  
Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors.  ...  Recent literature has demonstrated the applicability of a generalized form of Differential Privacy to provide guarantees over text queries.  ...  To get an intuition on how LAC can be used to improve utility while preserve privacy, consider the standard randomized response of (Warner 1965) . Given a bit b ∈ {0, 1} and privacy parameter ε.  ... 
doi:10.32473/flairs.v34i1.128461 fatcat:mcse4lzmjjfcveqgptzlgrttiy

RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL

Moushira Abdallah Mohamed Ahmed, Shuhui Wu, Laure Deveriane Dushime, Yuanhong Tao
2021 International Journal of Engineering Technologies and Management Research  
between privacy and utility in central and local model.  ...  Consequently, the usage of modified technique differential privacy federated learning with shuffle model will explores the gap between privacy and accuracy in both models.  ...  DP and LDP by reducing the gap between privacy and utility.  ... 
doi:10.29121/ijetmr.v8.i11.2021.1028 fatcat:2dlseelznndq3aoau64dcrnaby

Privacy-Preserving Bandits [article]

Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits
2020 arXiv   pre-print
Specifically, we observed only a decrease of 2.6 accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget ϵ≈ 0.693.  ...  Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage  ...  ACKNOWLEDGEMENTS Mohammad Malekzadeh wishes to thank the Life Sciences Institute at Queen Mary University of London and professor Andrea Caballero for their support through the project.  ... 
arXiv:1909.04421v4 fatcat:ynffzmb3czc33dxlkncocevyja

A survey on security and privacy issues in IoV

Tanvi Garg, Navid Kagalwalla, Prathamesh Churi, Ambika Pawar, Sanjay Deshmukh
2020 International Journal of Electrical and Computer Engineering (IJECE)  
We examine the various challenges in implementing security and privacy in IoV by reviewing past papers along with pointing out research gaps and possible future work and putting forth our on inferences  ...  Be that as it may, as the quantity of associated vehicles continues expanding, new prerequisites, (for example, consistent, secure, vigorous, versatile data trade among vehicles, people, and side of the  ...  user and therefore, the paper utilizes the concept of haystack privacy.  ... 
doi:10.11591/ijece.v10i5.pp5409-5419 fatcat:jbtkki2nlbh4vbsrxxsuyganc4

Selective MPC: Distributed Computation of Differentially Private Key Value Statistics [article]

Thomas Humphries, Rasoul Akhavan Mahdavi, Shannon Veitch, Florian Kerschbaum
2021 arXiv   pre-print
We show that our protocol satisfies pure DP and is provably secure in the combined DP/MPC model.  ...  An increasingly popular method for computing aggregate statistics while preserving users' privacy is local differential privacy (LDP).  ...  ACKNOWLEDGEMENTS We gratefully acknowledge the support of NSERC for grants RGPIN-05849, CRDPJ-531191, IRC-537591 and the Royal Bank of Canada for funding this research.  ... 
arXiv:2107.12407v1 fatcat:moorries4jhrfaddbelx7sj6ky

A Comprehensive Survey on Local Differential Privacy

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu
2020 Security and Communication Networks  
Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while  ...  With the advent of the era of big data, privacy issues have been becoming a hot topic in public.  ...  [132] proposed a variant of LDP based on d-privacy for distance-sensitive data, e.g., location data. e LDP based on d-privacy can improve the tradeoff between privacy and utility compared to standard  ... 
doi:10.1155/2020/8829523 fatcat:xjk3vgyambb5xioc2q5hyr2hua

DIFFERENTIAL PRIVACY FOR IOT-ENABLED CRITICAL INFRASTRUCTURE: A COMPREHENSIVE SURVEY

Muhammad Akbar Husnoo, Adnan Anwar, Ripon K. Chakrabortty, Robin Doss, Mike J. Ryan
2021 IEEE Access  
However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats.  ...  This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids  ...  schemes and improves the utility of the data.  ... 
doi:10.1109/access.2021.3124309 fatcat:vejtyjyrwffeffi7ob2o2svyja

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation [article]

Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista Bonawitz, Deborah Estrin, Marco Gruteser
2021 arXiv   pre-print
To achieve this, we revisit the distributed differential privacy concept based on recent results in the secure multiparty computation field and design a scalable and adaptive distributed differential privacy  ...  significantly smaller than existing state-of-the-art private protocols of similar accuracy.  ...  Acknowledgments At Cornell Tech, Bagdasaryan is supported in part by a Cornell Digital Life Initiative fellowship and an Apple Scholars in AI/ML fellowship.  ... 
arXiv:2111.02356v1 fatcat:ak4pnrnq5jfgpa3hbp5sycaf2i
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