Michael Hay

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 35

Top Topics

Differential Privacy

Differentially Private

Directed Graphical Models

Top Venues

SIGMOD Conference

Proc. VLDB Endow.

Publications

Marco Gaboardi, Michael Hay, Salil P. Vadhan
Programming Frameworks for Differential Privacy. CoRR (2024)
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 (2023)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality 13 (1) (2023)
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 (2022)
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 (2022)
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR (2021)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR (2021)
Samuel Haney, William Sexton, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity. CoRR (2021)
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) (2020)
Rachel Cummings, Michael Hay
TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy. CCS (2020)
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair decision making using privacy-protected data. FAT* (2020)
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) (2019)
Ios Kotsogiannis, Yuchao Tao, Ashwin Machanavajjhala, Gerome Miklau, Michael Hay
Architecting a Differentially Private SQL Engine. CIDR (2019)
Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. 12 (12) (2019)
Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair Decision Making using Privacy-Protected Data. CoRR (2019)
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) (2019)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. 11 (10) (2018)
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)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference (2018)
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala
Differentially Private Hierarchical Group Size Estimation. CoRR (2018)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Ektelo: A Framework for Defining Differentially-Private Computations. CoRR (2018)
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala
Differentially Private Hierarchical Count-of-Counts Histograms. Proc. VLDB Endow. 11 (11) (2018)
Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Optimizing error of high-dimensional statistical queries under differential privacy. CoRR (2018)
Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
PeGaSus: Data-Adaptive Differentially Private Stream Processing. CCS (2017)
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR (2017)
Ashwin Machanavajjhala, Xi He, Michael Hay
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges. SIGMOD Conference (2017)
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)
Michael Hay, Liudmila Elagina, Gerome Miklau
Differentially Private Rank Aggregation. SDM (2017)
Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Margaret Orr
DIAS: Differentially Private Interactive Algorithm Selection using Pythia. SIGMOD Conference (2017)
Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
Pythia: Data Dependent Differentially Private Algorithm Selection. SIGMOD Conference (2017)
Ashwin Machanavajjhala, Xi He, Michael Hay
Differential Privacy in the Wild: A tutorial on current practices & open challenges. Proc. VLDB Endow. 9 (13) (2016)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang
Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference (2016)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang, George Bissias
Exploring Privacy-Accuracy Tradeoffs using DPComp. SIGMOD Conference (2016)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang
Principled Evaluation of Differentially Private Algorithms using DPBench. CoRR (2015)
Chao Li, Gerome Miklau, Michael Hay, Andrew McGregor, Vibhor Rastogi
The matrix mechanism: optimizing linear counting queries under differential privacy. VLDB J. 24 (6) (2015)
Chao Li, Michael Hay, Gerome Miklau, Yue Wang
A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy. CoRR (2014)
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) (2014)
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)
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)
Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass
Crowd-Blending Privacy. CRYPTO (2012)
Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay
An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching CoRR (2012)
Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass
Crowd-Blending Privacy. IACR Cryptol. ePrint Arch. 2012 (2012)
Xiaokui Xiao, Gabriel Bender, Michael Hay, Johannes Gehrke
iReduct: differential privacy with reduced relative errors. SIGMOD Conference (2011)
Michael Hay, Kun Liu, Gerome Miklau, Jian Pei, Evimaria Terzi
Privacy-aware data management in information networks. SIGMOD Conference (2011)
Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu
Boosting the Accuracy of Differentially Private Histograms Through Consistency. Proc. VLDB Endow. 3 (1) (2010)
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, Andrew McGregor
Optimizing linear counting queries under differential privacy. PODS (2010)
Michael Hay, Basil Saeed, Chung-Horng Lung, Anand Srinivasan
Co-located Physical-Layer Network Coding to mitigate passive eavesdropping. PST (2010)
Michael Hay, Gerome Miklau, David D. Jensen, Donald F. Towsley, Chao Li
Resisting structural re-identification in anonymized social networks. VLDB J. 19 (6) (2010)
Vibhor Rastogi, Michael Hay, Gerome Miklau, Dan Suciu
Relationship privacy: output perturbation for queries with joins. PODS (2009)
Michael Hay, Chao Li, Gerome Miklau, David D. Jensen
Accurate Estimation of the Degree Distribution of Private Networks. ICDM (2009)
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, Andrew McGregor
Optimizing Histogram Queries under Differential Privacy CoRR (2009)
Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu
Boosting the Accuracy of Differentially-Private Queries Through Consistency CoRR (2009)
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) (2008)
Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay
An Integrated, Conditional Model of Information Extraction and Coreference with Appli. UAI (2004)
Jennifer Neville, David D. Jensen, Lisa Friedland, Michael Hay
Learning relational probability trees. KDD (2003)
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) (2003)
David D. Jensen, Jennifer Neville, Michael Hay
Avoiding Bias when Aggregating Relational Data with Degree Disparity. ICML (2003)