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Thakurta, Abhradeep
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1

Statistically Valid Inferences from Privacy-Protected Data:

EVANS, GEORGINA ; KING, GARY ; SCHWENZFEIER, MARGARET.
American Political Science Review.  117 (2023)  4 - p. 1275-1290 , 2023
Link: https://doi.org/10.1017/..
 
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2

Is Private Learning Possible with Instance Encoding?:

, In: 2021 IEEE Symposium on Security and Privacy (SP),
Carlini, Nicholas ; Deng, Samuel ; Garg, Sanjam... - p. 410-427 , 2021
Link: https://doi.org/10.1109/..
 
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3

Adversary Instantiation: Lower Bounds for Differentially Pr..:

, In: 2021 IEEE Symposium on Security and Privacy (SP),
Nasr, Milad ; Songi, Shuang ; Thakurta, Abhradeep.. - p. 866-882 , 2021
Link: https://doi.org/10.1109/..
 
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4

Privacy-preserving Data Mining in Industry:

, In: Companion Proceedings of The 2019 World Wide Web Conference,
Kenthapadi, Krishnaram ; Mironov, Ilya ; Thakurta, Abhradeep - p. 1308-1310 , 2019
Link: https://dl.acm.org/doi/1..
 
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5

Amplification by shuffling : from local to central diffe..:

, In: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms,
Erlingsson, Úlfar ; Feldman, Vitaly ; Mironov, Ilya... - p. 2468-2479 , 2019
Link: https://dl.acm.org/doi/1..
 
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6

Erasure-Resilient Property Testing:

Dixit, Kashyap ; Raskhodnikova, Sofya ; Thakurta, Abhradeep.
SIAM Journal on Computing.  47 (2018)  2 - p. 295-329 , 2018
Link: https://doi.org/10.1137/..
 
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7

Analyze gauss : optimal bounds for privacy-preserving pr..:

, In: Proceedings of the forty-sixth annual ACM symposium on Theory of computing,
Dwork, Cynthia ; Talwar, Kunal ; Thakurta, Abhradeep. - p. 11-20 , 2014
Link: https://dl.acm.org/doi/1..
 
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8

GUPT : privacy preserving data analysis made easy:

, In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data,
Mohan, Prashanth ; Thakurta, Abhradeep ; Shi, Elaine.. - p. 349-360 , 2012
Link: https://dl.acm.org/doi/1..
 
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9

Discovering frequent patterns in sensitive data:

, In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining,
Bhaskar, Raghav ; Laxman, Srivatsan ; Smith, Adam. - p. 503-512 , 2010
Link: https://dl.acm.org/doi/1..
 
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10

Privacy-preserving Data Mining in Industry:

, In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining,
Kenthapadi, Krishnaram ; Mironov, Ilya ; Thakurta, Abhradeep Guha - p. 840-841 , 2019
Link: https://dl.acm.org/doi/1..
 
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11

(Amplified) Banded Matrix Factorization: A unified approach..:

Choquette-Choo, Christopher A ; Ganesh, Arun ; McKenna, Ryan...
http://arxiv.org/abs/2306.08153.  , 2023
Link: http://arxiv.org/abs/230..
 
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12

Faster Differentially Private Convex Optimization via Secon..:

Ganesh, Arun ; Haghifam, Mahdi ; Steinke, Thomas.
http://arxiv.org/abs/2305.13209.  , 2023
Link: http://arxiv.org/abs/230..
 
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13

Challenges towards the Next Frontier in Privacy:

Cummings, Rachel ; Desfontaines, Damien ; Evans, David...
http://arxiv.org/abs/2304.06929.  , 2023
Link: http://arxiv.org/abs/230..
 
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14

Training Private Models That Know What They Don't Know:

Rabanser, Stephan ; Thudi, Anvith ; Thakurta, Abhradeep..
http://arxiv.org/abs/2305.18393.  , 2023
Link: http://arxiv.org/abs/230..
 
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15

Privacy Amplification for Matrix Mechanisms:

Choquette-Choo, Christopher A ; Ganesh, Arun ; Steinke, Thomas.
http://arxiv.org/abs/2310.15526.  , 2023
Link: http://arxiv.org/abs/231..
 
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