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2020 – today
- 2024
[i18]Marco Gaboardi
, Michael Hay, Salil P. Vadhan:
Programming Frameworks for Differential Privacy. CoRR abs/2403.11088 (2024)- 2023
- [j14]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality 13(1) (2023) - [i17]Temilola Adeleye, Skye Berghel, Damien Desfontaines, Michael Hay, Isaac Johnson, Cléo Lemoisson, Ashwin Machanavajjhala, Tom Magerlein, Gabriele Modena, David Pujol, Daniel Simmons-Marengo, Hal Triedman:
Publishing Wikipedia usage data with strong privacy guarantees. CoRR abs/2308.16298 (2023) - 2022
- [i16]Samuel Haney, Damien Desfontaines, Luke Hartman, Ruchit Shrestha, Michael Hay:
Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers. CoRR abs/2207.13793 (2022) - [i15]Skye Berghel, Philip Bohannon, Damien Desfontaines, Charles Estes, Samuel Haney, Luke Hartman, Michael Hay, Ashwin Machanavajjhala, Tom Magerlein, Gerome Miklau, Amritha Pai, William Sexton, Ruchit Shrestha:
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy. CoRR abs/2212.04133 (2022) - 2021
- [i14]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/2106.12118 (2021) - [i13]Samuel Haney, William Sexton, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity. CoRR abs/2107.10659 (2021) - [i12]Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR abs/2112.09238 (2021) - 2020
- [j13]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
ϵKTELO: A Framework for Defining Differentially Private Computations. ACM Trans. Database Syst. 45(1): 2:1-2:44 (2020) - [c25]Rachel Cummings, Michael Hay:
TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. CCS 2020: 2153-2154 - [c24]David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair decision making using privacy-protected data. FAT* 2020: 189-199
2010 – 2019
- 2019
- [j12]Ios Kotsogiannis, Yuchao Tao, Xi He, Maryam Fanaeepour, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
PrivateSQL: A Differentially Private SQL Query Engine. Proc. VLDB Endow. 12(11): 1371-1384 (2019) - [j11]Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. 12(12): 1918-1921 (2019) - [j10]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
#8712;: A Framework for Defining Differentially-Private Computations. SIGMOD Rec. 48(1): 15-22 (2019) - [c23]Ios Kotsogiannis, Yuchao Tao, Ashwin Machanavajjhala, Gerome Miklau, Michael Hay:
Architecting a Differentially Private SQL Engine. CIDR 2019 - [i11]Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair Decision Making using Privacy-Protected Data. CoRR abs/1905.12744 (2019) - 2018
- [j9]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. 11(10): 1206-1219 (2018) - [j8]Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala:
Differentially Private Hierarchical Count-of-Counts Histograms. Proc. VLDB Endow. 11(11): 1509-1521 (2018) - [c22]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference 2018: 115-130 - [c21]Sameera Ghayyur, Yan Chen, Roberto Yus, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau, Sharad Mehrotra:
IoT-Detective: Analyzing IoT Data Under Differential Privacy. SIGMOD Conference 2018: 1725-1728 - [i10]Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala:
Differentially Private Hierarchical Group Size Estimation. CoRR abs/1804.00370 (2018) - [i9]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/1808.03537 (2018) - [i8]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Ektelo: A Framework for Defining Differentially-Private Computations. CoRR abs/1808.03555 (2018) - 2017
- [c20]Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
PeGaSus: Data-Adaptive Differentially Private Stream Processing. CCS 2017: 1375-1388 - [c19]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML 2017: 478-487 - [c18]Michael Hay, Liudmila Elagina, Gerome Miklau:
Differentially Private Rank Aggregation. SDM 2017: 669-677 - [c17]Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
Pythia: Data Dependent Differentially Private Algorithm Selection. SIGMOD Conference 2017: 1323-1337 - [c16]Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Margaret Orr:
DIAS: Differentially Private Interactive Algorithm Selection using Pythia. SIGMOD Conference 2017: 1679-1682 - [c15]Ashwin Machanavajjhala, Xi He, Michael Hay:
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges. SIGMOD Conference 2017: 1727-1730 - [i7]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR abs/1706.04646 (2017) - 2016
- [j7]Ashwin Machanavajjhala, Xi He, Michael Hay:
Differential Privacy in the Wild: A tutorial on current practices & open challenges. Proc. VLDB Endow. 9(13): 1611-1614 (2016) - [c14]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang:
Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154 - [c13]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang, George Bissias:
Exploring Privacy-Accuracy Tradeoffs using DPComp. SIGMOD Conference 2016: 2101-2104 - 2015
- [j6]Chao Li, Gerome Miklau, Michael Hay, Andrew McGregor, Vibhor Rastogi:
The matrix mechanism: optimizing linear counting queries under differential privacy. VLDB J. 24(6): 757-781 (2015) - [i6]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang:
Principled Evaluation of Differentially Private Algorithms using DPBench. CoRR abs/1512.04817 (2015) - 2014
- [j5]Chao Li, Michael Hay, Gerome Miklau, Yue Wang:
A Data- and Workload-Aware Query Answering Algorithm for Range Queries Under Differential Privacy. Proc. VLDB Endow. 7(5): 341-352 (2014) - [i5]Chao Li, Michael Hay, Gerome Miklau, Yue Wang:
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy. CoRR abs/1410.0265 (2014) - 2013
- [c12]Michael Hay, Basil Saeed, Chung-Horng Lung, Thomas Kunz, Anand Srinivasan:
Network Coding and Quality of Service metrics for Mobile Ad-hoc Networks. IWCMC 2013: 521-526 - [c11]Ann Irvine, Darakhshan Mir, Michael Hay:
How PhD students at research universities can prepare for a career at a liberal arts college (abstract only). SIGCSE 2013: 752 - 2012
- [c10]Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass:
Crowd-Blending Privacy. CRYPTO 2012: 479-496 - [i4]Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay:
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching. CoRR abs/1207.4157 (2012) - [i3]Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass:
Crowd-Blending Privacy. IACR Cryptol. ePrint Arch. 2012: 456 (2012) - 2011
- [c9]Xiaokui Xiao, Gabriel Bender, Michael Hay, Johannes Gehrke:
iReduct: differential privacy with reduced relative errors. SIGMOD Conference 2011: 229-240 - [c8]Michael Hay, Kun Liu, Gerome Miklau, Jian Pei, Evimaria Terzi:
Privacy-aware data management in information networks. SIGMOD Conference 2011: 1201-1204 - 2010
- [j4]Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu:
Boosting the Accuracy of Differentially Private Histograms Through Consistency. Proc. VLDB Endow. 3(1): 1021-1032 (2010) - [j3]Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Chao Li:
Resisting structural re-identification in anonymized social networks. VLDB J. 19(6): 797-823 (2010) - [c7]Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, Andrew McGregor:
Optimizing linear counting queries under differential privacy. PODS 2010: 123-134 - [c6]Michael Hay, Basil Saeed, Chung-Horng Lung, Anand Srinivasan:
Co-located Physical-Layer Network Coding to mitigate passive eavesdropping. PST 2010: 1-2
2000 – 2009
- 2009
- [c5]Michael Hay, Chao Li, Gerome Miklau, David D. Jensen:
Accurate Estimation of the Degree Distribution of Private Networks. ICDM 2009: 169-178 - [c4]Vibhor Rastogi, Michael Hay, Gerome Miklau, Dan Suciu:
Relationship privacy: output perturbation for queries with joins. PODS 2009: 107-116 - [i2]Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu:
Boosting the Accuracy of Differentially-Private Queries Through Consistency. CoRR abs/0904.0942 (2009) - [i1]Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, Andrew McGregor:
Optimizing Histogram Queries under Differential Privacy. CoRR abs/0912.4742 (2009) - 2008
- [j2]Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Philipp Weis:
Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1(1): 102-114 (2008) - 2004
- [c3]Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay:
An Integrated, Conditional Model of Information Extraction and Coreference with Appli. UAI 2004: 593-601 - 2003
- [j1]Amy McGovern, Lisa Friedland, Michael Hay, Brian Gallagher, Andrew S. Fast, Jennifer Neville, David D. Jensen:
Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explor. 5(2): 165-172 (2003) - [c2]David D. Jensen, Jennifer Neville, Michael Hay:
Avoiding Bias when Aggregating Relational Data with Degree Disparity. ICML 2003: 274-281 - [c1]Jennifer Neville, David D. Jensen, Lisa Friedland, Michael Hay:
Learning relational probability trees. KDD 2003: 625-630
Coauthor Index
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last updated on 2025-01-21 00:22 CET by the dblp team
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