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Differential Privacy Papers
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TitleAuthorsURLClaimed by:
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General Techniques / Systems
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Local Privacy and Statistical Minimax RatesDuchi, Jordan, Wainwrighthttps://stanford.edu/~jduchi/projects/DuchiJoWa13_focs.pdf
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Prochlo: Strong Privacy for Analytics in the CrowdBittau et al.https://arxiv.org/pdf/1710.00901Arkady Y.
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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal ResponseErlinsson, Pihur, Korolovahttps://arxiv.org/pdf/1407.6981Minghui Xu
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlinsson et al.https://arxiv.org/pdf/1811.12469
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Distributed Differential Privacy via ShufflingCheu et al.https://arxiv.org/pdf/1808.01394
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Privacy Amplification by IterationFeldman et al.https://arxiv.org/pdf/1808.06651.pdfThinh Dang
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The Johnson-Lindenstrauss Transform Itself Preserves Differential PrivacyBlocki et al.https://arxiv.org/pdf/1204.2136
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Locally Private Hypothesis TestingSheffethttps://arxiv.org/pdf/1802.03441
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Local Differential Privacy for Evolving DataJoseph et al.https://arxiv.org/pdf/1802.07128
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Optimizing error of high-dimensional statistical queries under differential privacy
McKenna et al.https://arxiv.org/pdf/1808.03537
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Graphical-model based estimation and inference for differential privacyMcKenna et al.https://arxiv.org/pdf/1901.09136
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Utility Preserving Secure Private Data ReleaseDhaliwal et al.https://arxiv.org/pdf/1901.09858
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Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Wu et al.https://arxiv.org/pdf/1606.04722.pdf
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Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Balle et al.https://arxiv.org/pdf/1807.01647.pdf
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Renyi Differential PrivacyMironovhttps://arxiv.org/pdf/1702.07476.pdf
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Differentially Private Messaging
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Vuvuzela: Scalable Private Messaging Resistant to Traffic Analysisvan den Hooff, et al.https://www.freehaven.net/anonbib/cache/vuvuzela:sosp15.pdfTianyu Lyu
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Stadium: A Distributed Metadata-Private Messaging SystemTyagi et al.https://eprint.iacr.org/2016/943.pdfZhaoqi Zhang
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Karaoke: Distributed Private Messaging Immune to Passive Traffic AnalysisLazar et al.https://people.csail.mit.edu/nickolai/papers/lazar-karaoke.pdfAnastasija M.
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Applications
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Towards Practical Differentially Private Convex OptimizationIyengar et al.
https://csdl.computer.org/csdl/proceedings/sp/2019/6660/00/666000a001.pdf
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Incentive-Aware Learning for Large MarketsEpasto et al.https://ai.google/research/pubs/pub46913Anasrasija M.
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Differentially Private Ordinary Least SquaresSheffethttps://arxiv.org/pdf/1507.02482
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Differentially Private Combinatorial OptimizationGupta et al.http://www.cse.psu.edu/~ads22/privacy598/papers/glmrt10.pdfZikai Zhang
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Differentially Private Data Analysis of Social Networks via Restricted SensitivityBlocki et al.https://arxiv.org/pdf/1208.4586Jack A.
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On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Abadi et al.https://arxiv.org/pdf/1708.08022
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Local, Private, Efficient Protocols for Succinct HistogramsBassily, Smithhttps://arxiv.org/pdf/1504.04686
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Practical Locally Private Heavy HittersBassily et al.https://arxiv.org/pdf/1707.04982
This paper builds on the one above it
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Differentially Private Continual Release of Graph StatisticsSong et al.https://arxiv.org/pdf/1809.02575Chunchi Liu
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Learning with Privacy at ScaleApple
https://machinelearning.apple.com/docs/learning-with-privacy-at-scale/appledifferentialprivacysystem.pdf
Liran Cohen
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Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis
Dwork et al.http://kunaltalwar.org/papers/PrivatePCA.pdfMinghui Xu
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Mitigating Storage Side Channels Using Statistical Privacy MechanismsXiao et al.https://www.cs.unc.edu/~reiter/papers/2015/CCS1.pdfHonglu Jiang
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Towards Practical Differential Privacy for SQL QueriesNoah Johnson et al.https://arxiv.org/pdf/1706.09479.pdfNate Jensen
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