World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
9156
World Ranking
7896
National Ranking
3409

Overview

Gerome Miklau is affiliated with the University of Massachusetts Amherst in the United States. Their research primarily focuses on computer science, with a distinct emphasis on artificial intelligence and privacy-preserving technologies in data.

The scientist's work spans several subfields including artificial intelligence, computer networks and communications, electrical and electronic engineering, epidemiology, and computer science applications. Their research intersects multiple topics such as privacy-preserving technologies in data, cryptography and data security, stochastic gradient optimization techniques, vehicular ad hoc networks (VANETs), mobile crowdsensing and crowdsourcing, privacy, security, and data protection, as well as data-driven disease surveillance.

Miklau's recent research publications include:

  • "Winning the NIST Contest: A scalable and general approach to differentially private synthetic data" (2021), Journal of Privacy and Confidentiality
  • "AIM" (2022), Proceedings of the VLDB Endowment
  • "Benchmarking Differentially Private Synthetic Data Generation Algorithms" (2021), arXiv (Cornell University)
  • "Investigating Visual Analysis of Differentially Private Data" (2020), IEEE Transactions on Visualization and Computer Graphics
  • "AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data" (2022), arXiv (Cornell University)

Frequent publication venues for Miklau's work include arXiv (Cornell University), the Journal of Privacy and Confidentiality, the Proceedings of the VLDB Endowment, IEEE Transactions on Visualization and Computer Graphics, and ACM Transactions on Database Systems.

Collaborative work is notable in Miklau's career. Frequent co-authors are Ashwin Machanavajjhala, Ryan McKenna, Michael Hay, Daniel Sheldon, and Sam Haney. These collaborations highlight an active engagement with peers in advancing topics related to data privacy and security.

Best Publications

  • Resisting structural re-identification in anonymized social networks

    Michael Hay;Gerome Miklau;David Jensen;Don Towsley

  • Boosting the accuracy of differentially private histograms through consistency

    Michael Hay;Vibhor Rastogi;Gerome Miklau;Dan Suciu

  • Anonymizing Social Networks

    Michael Hay;Gerome Miklau;David Jensen;Philipp Weis

  • Accurate Estimation of the Degree Distribution of Private Networks

    Michael Hay;Chao Li;Gerome Miklau;David Jensen

  • Optimizing linear counting queries under differential privacy

    Chao Li;Michael Hay;Vibhor Rastogi;Gerome Miklau

  • Controlling access to published data using cryptography

    Gerome Miklau;Dan Suciu

  • Containment and equivalence for a fragment of XPath

    Gerome Miklau;Dan Suciu

  • Containment and equivalence for an XPath fragment

    Gerome Miklau;Dan Suciu

  • Processing XML Streams with Deterministic Automata

    Todd J. Green;Gerome Miklau;Makoto Onizuka;Dan Suciu

  • A formal analysis of information disclosure in data exchange

    Gerome Miklau;Dan Suciu

  • Processing XML streams with deterministic automata and stream indexes

    Todd J. Green;Ashish Gupta;Gerome Miklau;Makoto Onizuka

  • The Piazza peer data management project

    Igor Tatarinov;Zachary Ives;Jayant Madhavan;Alon Halevy

  • A formal analysis of information disclosure in data exchange

    Gerome Miklau;Dan Suciu

  • A Theory of Pricing Private Data

    Chao Li;Daniel Yang Li;Gerome Miklau;Dan Suciu

  • A data- and workload-aware algorithm for range queries under differential privacy

    Chao Li;Michael Hay;Gerome Miklau;Yue Wang

  • Relationship privacy: output perturbation for queries with joins

    Vibhor Rastogi;Michael Hay;Gerome Miklau;Dan Suciu

  • A theory of pricing private data

    Chao Li;Daniel Yang Li;Gerome Miklau;Dan Suciu

  • Principled Evaluation of Differentially Private Algorithms using DPBench

    Michael Hay;Ashwin Machanavajjhala;Gerome Miklau;Yan Chen

  • Differential privacy in data publication and analysis

    Yin Yang;Zhenjie Zhang;Gerome Miklau;Marianne Winslett

  • An adaptive mechanism for accurate query answering under differential privacy

    Chao Li;Gerome Miklau

  • The matrix mechanism: optimizing linear counting queries under differential privacy

    Chao Li;Gerome Miklau;Michael Hay;Andrew Mcgregor

Frequent Co-Authors

Dan Suciu
Dan Suciu University of Washington
Ashwin Machanavajjhala
Ashwin Machanavajjhala Duke University
Serge Abiteboul
Serge Abiteboul École Normale Supérieure
Hosagrahar V. Jagadish
Hosagrahar V. Jagadish University of Michigan–Ann Arbor
David Jensen
David Jensen University of Massachusetts Amherst
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Brian Neil Levine
Brian Neil Levine University of Massachusetts Amherst
Bill Howe
Bill Howe University of Washington
Don Towsley
Don Towsley University of Massachusetts Amherst
Andrew McGregor
Andrew McGregor University of Massachusetts Amherst

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