Michael Hay
Impact in
- Computer Science Applications top 0.5%
- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Internet Traffic Analysis and Secure E-voting
- Stochastic Gradient Optimization Techniques
Papers in
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- Mobile Crowdsensing and Crowdsourcing 7
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- Privacy-Preserving Technologies in Data 27
- Cryptography and Data Security 20
- Internet Traffic Analysis and Secure E-voting 8
- Stochastic Gradient Optimization Techniques 4
- Bayesian Modeling and Causal Inference 3
- Co-authors
- Gerome MiklauDavid JensenVibhor RastogiChao LiDan SuciuDon TowsleyAshwin MachanavajjhalaAndrew McGregor
- Journals
- Proceedings of the VLDB Endowment (8 papers)The VLDB Journal (2 papers)Industrial and Organizational Psychology (1 paper)ACM Transactions on Database Systems (1 paper)The Journal of Defense Modeling and Simulation Applications Methodology Technology (1 paper)
- Partner nations
- United StatesCanadaSingapore
In The Last Decade
Michael Hay
37 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Computer Science Applications 421
- Artificial Intelligence 2.1k
- Sociology and Political Science 832
- Transportation 106
- Management Science and Operations Research 187
Countries citing papers authored by Michael Hay
This map shows the geographic impact of Michael Hay's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Hay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Hay more than expected).
Fields of papers citing papers by Michael Hay
This network shows the impact of papers produced by Michael Hay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Hay. The network helps show where Michael Hay may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Hay, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | 2020 | 44 | |
| 3 | Architecting a Differentially Private SQL Engine. | 2019 | 8 |
| 4 | 2019 | 4 | |
| 5 | 2018 | 18 | |
| 6 | 2018 | 14 | |
| 7 | 2017 | 43 | |
| 8 | 2017 | 25 | |
| 9 | 2016 | 10 | |
| 10 | 2016 | 72 | |
| 11 | 2014 | 6 | |
| 12 | 2013 | 6 | |
| 13 | 2011 | 94 | |
| 14 | 2010 | 4 | |
| 15 | 2009 | 220 | |
| 16 | Boosting the Accuracy of Differentially-Private Queries Through Consistency | 2009 | 15 |
| 17 | 2004 | 77 | |
| 18 | Avoiding bias when aggregating relational data with degree disparity | 2003 | 22 |
| 19 | 2003 | 8 | |
| 20 | 2003 | 36 |
About Michael Hay
Michael Hay is a scholar working on Computer Science Applications, Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications and Sociology and Political Science, having authored 37 papers that have together received 2.3k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (27 papers), Cryptography and Data Security (20 papers), Internet Traffic Analysis and Secure E-voting (8 papers), Privacy, Security, and Data Protection (8 papers), Mobile Crowdsensing and Crowdsourcing (7 papers), Data Quality and Management (4 papers), Stochastic Gradient Optimization Techniques (4 papers) and Bayesian Modeling and Causal Inference (3 papers). The work is most often cited by research in Computer Science Applications (421 citations), Artificial Intelligence (2.1k citations), Sociology and Political Science (832 citations), Transportation (106 citations) and Management Science and Operations Research (187 citations). Michael Hay has collaborated with scholars based in United States, Canada and Singapore. Frequent co-authors include Gerome Miklau, David Jensen, Vibhor Rastogi, Chao Li, Dan Suciu, Don Towsley, Ashwin Machanavajjhala, Andrew McGregor, Jennifer Neville and Lisa Friedland. Their work appears in journals such as Proceedings of the VLDB Endowment, The VLDB Journal, Industrial and Organizational Psychology, ACM Transactions on Database Systems and The Journal of Defense Modeling and Simulation Applications Methodology Technology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.