Ashwin Machanavajjhala

ORCID

Publication Activity (10 Years)

Years Active: 2004-2023
Publications (10 Years): 90

Top Venues

Publications

Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. Proc. VLDB Endow. 17 (1) (2023)
Jeremy Seeman, William Sexton, David Pujol, Ashwin Machanavajjhala
Privately Answering Queries on Skewed Data via Per Record Differential Privacy. CoRR (2023)
Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
Explaining Differentially Private Query Results With DPXPlain. Proc. VLDB Endow. 16 (12) (2023)
Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. CoRR (2023)
Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys. SIGMOD Rec. 52 (1) (2023)
Yanping Zhang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala
Longshot: Indexing Growing Databases using MPC and Differential Privacy. Proc. VLDB Endow. 16 (8) (2023)
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)
Chenghong Wang, David Pujol, Kartik Nayak, Ashwin Machanavajjhala
Private Proof-of-Stake Blockchains using Differentially-Private Stake Distortion. USENIX Security Symposium (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)
David Pujol, Amir Gilad, Ashwin Machanavajjhala
PreFair: Privately Generating Justifiably Fair Synthetic Data. Proc. VLDB Endow. 16 (6) (2023)
Chenghong Wang, David Pujol, Kartik Nayak, Ashwin Machanavajjhala
Private Proof-of-Stake Blockchains using Differentially-private Stake Distortion. IACR Cryptol. ePrint Arch. 2023 (2023)
David Pujol, Albert Sun, Brandon Fain, Ashwin Machanavajjhala
Multi-Analyst Differential Privacy for Online Query Answering. Proc. VLDB Endow. 16 (4) (2022)
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
DPXPlain: Privately Explaining Aggregate Query Answers. Proc. VLDB Endow. 16 (1) (2022)
David Pujol, Albert Sun, Brandon Fain, Ashwin Machanavajjhala
Multi-Analyst Differential Privacy for Online Query Answering. CoRR (2022)
Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys. SIGMOD Conference (2022)
John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson L. Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence, Pavel Zhuravlev
The 2020 Census Disclosure Avoidance System TopDown Algorithm. CoRR (2022)
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)
Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. SIGMOD Conference (2022)
Daniel Kifer, John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Philip Leclerc, Ashwin Machanavajjhala, William Sexton, Pavel Zhuravlev
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. CoRR (2022)
David Pujol, Amir Gilad, Ashwin Machanavajjhala
PreFair: Privately Generating Justifiably Fair Synthetic Data. CoRR (2022)
Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. CoRR (2022)
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
DPXPlain: Privately Explaining Aggregate Query Answers. CoRR (2022)
Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala
Synthesizing Linked Data Under Cardinality and Integrity Constraints. CoRR (2021)
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR (2021)
Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy. SIGMOD Conference (2021)
David Pujol, Ashwin Machanavajjhala
Equity and Privacy: More Than Just a Tradeoff. CoRR (2021)
Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy. CoRR (2021)
Sameer Wagh, Xi He, Ashwin Machanavajjhala, Prateek Mittal
DP-cryptography: marrying differential privacy and cryptography in emerging applications. Commun. ACM 64 (2) (2021)
Xi He, Jennie Rogers, Johes Bater, Ashwin Machanavajjhala, Chenghong Wang, Xiao Wang
Practical Security and Privacy for Database Systems. SIGMOD Conference (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)
Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala
Synthesizing Linked Data Under Cardinality and Integrity Constraints. SIGMOD Conference (2021)
David Pujol, Ashwin Machanavajjhala
Equity and Privacy: More Than Just a Tradeoff. IEEE Secur. Priv. 19 (6) (2021)
David Pujol, Yikai Wu, Brandon Fain, Ashwin Machanavajjhala
Budget Sharing for Multi-Analyst Differential Privacy. Proc. VLDB Endow. 14 (10) (2021)
Yuchao Tao, Johes Bater, Ashwin Machanavajjhala
Prior-Aware Distribution Estimation for Differential Privacy. CoRR (2021)
Yuchao Tao, Xi He, Ashwin Machanavajjhala, Sudeepa Roy
Computing Local Sensitivities of Counting Queries with Joins. CoRR (2020)
Yuchao Tao, Xi He, Ashwin Machanavajjhala, Sudeepa Roy
Computing Local Sensitivities of Counting Queries with Joins. SIGMOD Conference (2020)
David Pujol, Yikai Wu, Brandon Fain, Ashwin Machanavajjhala
Budget Sharing for Multi-Analyst Differential Privacy. CoRR (2020)
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)
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair decision making using privacy-protected data. FAT* (2020)
Ios Kotsogiannis, Stelios Doudalis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra
One-sided Differential Privacy. ICDE (2020)
Yanping Zhang, Chenghong Wang, David Pujol, Johes Bater, Matthew Lentz, Ashwin Machanavajjhala, Kartik Nayak, Lavanya Vasudevan, Jun Yang
Poirot: private contact summary aggregation: poster abstract. SenSys (2020)
Sameer Wagh, Xi He, Ashwin Machanavajjhala, Prateek Mittal
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications. CoRR (2020)
Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha
Crypt?: Crypto-Assisted Differential Privacy on Untrusted Servers. SIGMOD Conference (2020)
Nisarg Raval, Ashwin Machanavajjhala, Jerry Pan
Olympus: Sensor Privacy through Utility Aware Obfuscation. Proc. Priv. Enhancing Technol. 2019 (1) (2019)
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)
Nisarg Raval, Ali Razeen, Ashwin Machanavajjhala, Landon P. Cox, Andrew Warfield
Permissions Plugins as Android Apps. MobiSys (2019)
Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair Decision Making using Privacy-Protected Data. CoRR (2019)
Yikai Wu, David Pujol, Ios Kotsogiannis, Ashwin Machanavajjhala
Answering Summation Queries for Numerical Attributes under Differential Privacy. CoRR (2019)
Jennie Rogers, Johes Bater, Xi He, Ashwin Machanavajjhala, Madhav Suresh, Xiao Wang
Privacy Changes Everything. Poly/DMAH@VLDB (2019)
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
Capacity Bounded Differential Privacy. NeurIPS (2019)
Maryam Fanaeepour, Ashwin Machanavajjhala
PrivStream: Differentially Private Event Detection on Data Streams. CODASPY (2019)
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
Capacity Bounded Differential Privacy. CoRR (2019)
Chang Ge, Xi He, Ihab F. Ilyas, Ashwin Machanavajjhala
APEx: Accuracy-Aware Differentially Private Data Exploration. SIGMOD Conference (2019)
Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha
Outis: Crypto-Assisted Differential Privacy on Untrusted Servers. 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)
Andrew David Foote, Ashwin Machanavajjhala, Kevin McKinney
Releasing Earnings Distributions using Differential Privacy - Disclosure Avoidance System For Post-Secondary Employment Outcomes (PSEO). J. Priv. Confidentiality 9 (2) (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)
Yan Chen, Andrés F. Barrientos, Ashwin Machanavajjhala, Jerome P. Reiter
Is my model any good: differentially private regression diagnostics. Knowl. Inf. Syst. 54 (1) (2018)
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference (2018)
Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, Jennie Rogers
Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. CoRR (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)
Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, Jennie Rogers
ShrinkWrap: Efficient SQL Query Processing in Differentially Private Data Federations. Proc. VLDB Endow. 12 (3) (2018)
Xi He, Ashwin Machanavajjhala, Cheryl J. Flynn, Divesh Srivastava
Composing Differential Privacy and Secure Computation: A Case Study on Scaling Private Record Linkage. CCS (2017)
Chang Ge, Ihab F. Ilyas, Xi He, Ashwin Machanavajjhala
Private Exploration Primitives for Data Cleaning. CoRR (2017)
Christopher Streiffer, Animesh Srivastava, Victor Orlikowski, Yesenia Velasco, Vincentius Martin, Nisarg Raval, Ashwin Machanavajjhala, Landon P. Cox
ePrivateeye: to the edge and beyond! SEC (2017)
Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
PeGaSus: Data-Adaptive Differentially Private Stream Processing. CCS (2017)
Stelios Doudalis, Ios Kotsogiannis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra
One-sided Differential Privacy. CoRR (2017)
Xi He, Ashwin Machanavajjhala, Cheryl J. Flynn, Divesh Srivastava
Scaling Private Record Linkage using Output Constrained Differential Privacy. CoRR (2017)
Ashwin Machanavajjhala, Xi He, Michael Hay
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges. SIGMOD Conference (2017)
Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Margaret Orr
DIAS: Differentially Private Interactive Algorithm Selection using Pythia. SIGMOD Conference (2017)
John M. Abowd, Lorenzo Alvisi, Cynthia Dwork, Sampath Kannan, Ashwin Machanavajjhala, Jerome P. Reiter
Privacy-Preserving Data Analysis for the Federal Statistical Agencies. CoRR (2017)
Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, Mark Kutzbach, Lars Vilhuber
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics. SIGMOD Conference (2017)
Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
Pythia: Data Dependent Differentially Private Algorithm Selection. SIGMOD Conference (2017)
Ios Kotsogiannis, Elena Zheleva, Ashwin Machanavajjhala
Directed Edge Recommender System. WSDM (2017)
Nisarg Raval, Ashwin Machanavajjhala, Landon P. Cox
Protecting Visual Secrets Using Adversarial Nets. CVPR Workshops (2017)
Nisarg Raval, Animesh Srivastava, Ali Razeen, Kiron Lebeck, Ashwin Machanavajjhala, Landon P. Cox
What You Mark is What Apps See. MobiSys (2016)
Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, Andrés F. Barrientos
Differentially Private Regression Diagnostics. ICDM (2016)
Nisarg Raval, Animesh Srivastava, Ali Razeen, Kiron Lebeck, Ashwin Machanavajjhala, Landon P. Cox
Demo: What You Mark is What Apps See. MobiSys (Companion Volume) (2016)
Ashwin Machanavajjhala, Xi He, Michael Hay
Differential Privacy in the Wild: A tutorial on current practices & open challenges. Proc. VLDB Endow. 9 (13) (2016)
Benjamin Stoddard, Kate O'Hanlon, Brian Lin, Ashwin Machanavajjhala, Landon P. Cox
Ayumu: Efficient lifelogging with focused tasks. MobiCASE (2016)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang
Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference (2016)
Xi He, Nisarg Raval, Ashwin Machanavajjhala
A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories. Proc. VLDB Endow. 9 (13) (2016)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang, George Bissias
Exploring Privacy-Accuracy Tradeoffs using DPComp. SIGMOD Conference (2016)
Samuel Haney, Ashwin Machanavajjhala, Bolin Ding
Design of Policy-Aware Differentially Private Algorithms. Proc. VLDB Endow. 9 (4) (2015)
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang
Principled Evaluation of Differentially Private Algorithms using DPBench. CoRR (2015)
Ashwin Machanavajjhala, Daniel Kifer
Designing statistical privacy for your data. Commun. ACM 58 (3) (2015)
Yan Chen, Ashwin Machanavajjhala
On the Privacy Properties of Variants on the Sparse Vector Technique. CoRR (2015)
Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M. Procopiuc, Divesh Srivastava
DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems. Proc. VLDB Endow. 8 (11) (2015)
Ben Stoddard, Yan Chen, Ashwin Machanavajjhala
Differentially Private Algorithms for Empirical Machine Learning. CoRR (2014)
Nisarg Raval, Landon P. Cox, Animesh Srivastava, Ashwin Machanavajjhala, Kiron Lebeck
MarkIt: privacy markers for protecting visual secrets. UbiComp Adjunct (2014)
Samuel Haney, Ashwin Machanavajjhala, Bolin Ding
Answering Query Workloads with Optimal Error under Blowfish Privacy. CoRR (2014)
Daniel Kifer, Ashwin Machanavajjhala
Pufferfish: A framework for mathematical privacy definitions. ACM Trans. Database Syst. 39 (1) (2014)
Hye-Chung Kum, Ashok K. Krishnamurthy, Ashwin Machanavajjhala, Stanley C. Ahalt
Social Genome: Putting Big Data to Work for Population Informatics. Computer 47 (1) (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ashwin Machanavajjhala