Kobbi Nissim

ORCID

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 76

Top Venues

CoRR

J. Priv. Confidentiality

CCS

NeurIPS

Publications

Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Data Reconstruction: When You See It and When You Don't. CoRR (2024)
Roi Livni, Shay Moran, Kobbi Nissim, Chirag Pabbaraju
Credit Attribution and Stable Compression. CoRR (2024)
Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
On Differentially Private Online Predictions. CoRR (2023)
Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
Private Everlasting Prediction. CoRR (2023)
Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
Black-Box Differential Privacy for Interactive ML. NeurIPS (2023)
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Adaptive Data Analysis in a Balanced Adversarial Model. NeurIPS (2023)
Katrina Ligett, Kobbi Nissim
We Need to Focus on How Our Data Is Used, Not Just How It Is Shared. Commun. ACM 66 (9) (2023)
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Adaptive Data Analysis in a Balanced Adversarial Model. CoRR (2023)
Lina Qiu, Georgios Kellaris, Nikos Mamoulis, Kobbi Nissim, George Kollios
Doquet: Differentially Oblivious Range and Join Queries with Private Data Structures. Proc. VLDB Endow. 16 (13) (2023)
Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
Private Everlasting Prediction. NeurIPS (2023)
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. STOC (2022)
Kobbi Nissim, Chao Yan
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model. J. Priv. Confidentiality 12 (2) (2022)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. SIAM J. Comput. 50 (3) (2021)
Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. CRYPTO (3) (2021)
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds. CoRR (2021)
Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
Separating Adaptive Streaming from Oblivious Streaming. CoRR (2021)
Kobbi Nissim, Chao Yan
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model. CoRR (2021)
Kobbi Nissim
Privacy: From Database Reconstruction to Legal Theorems. PODS (2021)
Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols. CoRR (2021)
Dmytro Bogatov, Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill
εpsolute: Efficiently Querying Databases While Providing Differential Privacy. CCS (2021)
Kobbi Nissim, Alexandra Wood
Foundations for Robust Data Protection: Co-designing Law and Computer Science. TPS-ISA (2021)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Learning Privately with Labeled and Unlabeled Examples. Algorithmica 83 (1) (2021)
Marco Gaboardi, Kobbi Nissim, David Purser
The Complexity of Verifying Loop-Free Programs as Differentially Private. ICALP (2020)
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. ITC (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. TCC (2) (2020)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
Private Summation in the Multi-Message Shuffle Model. CCS (2020)
Aloni Cohen, Kobbi Nissim
Linear Program Reconstruction in Practice. J. Priv. Confidentiality 10 (1) (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. CoRR (2020)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
Private Summation in the Multi-Message Shuffle Model. CoRR (2020)
Sujata Banerjee, Yiling Chen, Kobbi Nissim, David Parkes, Katie Siek, Lauren Wilcox
Modernizing Data Control: Making Personal Digital Data Mutually Beneficial for Citizens and Industry. CoRR (2020)
Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols. SIAM J. Comput. 49 (6) (2020)
Cynthia Dwork, Alan F. Karr, Kobbi Nissim, Lars Vilhuber
On Privacy in the Age of COVID-19. J. Priv. Confidentiality 10 (2) (2020)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21 (2020)
Aloni Cohen, Kobbi Nissim
Towards formalizing the GDPR's notion of singling out. Proc. Natl. Acad. Sci. USA 117 (15) (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. IACR Cryptol. ePrint Arch. 2020 (2020)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Characterizing the Sample Complexity of Pure Private Learners. J. Mach. Learn. Res. 20 (2019)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
Improved Summation from Shuffling. CoRR (2019)
Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
Private Center Points and Learning of Halfspaces. CoRR (2019)
Mark Bun, Kobbi Nissim, Uri Stemmer
Simultaneous Private Learning of Multiple Concepts. J. Mach. Learn. Res. 20 (2019)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
The Privacy Blanket of the Shuffle Model. CoRR (2019)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
The Privacy Blanket of the Shuffle Model. CRYPTO (2) (2019)
Aloni Cohen, Kobbi Nissim
Towards Formalizing the GDPR's Notion of Singling Out. CoRR (2019)
Marco Gaboardi, Kobbi Nissim, David Purser
The Complexity of Verifying Circuits as Differentially Private. CoRR (2019)
Amos Beimel, Kobbi Nissim, Mohammad Zaheri
Exploring Differential Obliviousness. APPROX-RANDOM (2019)
Uri Stemmer, Kobbi Nissim
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. J. Priv. Confidentiality 9 (1) (2019)
Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim
Differentially Private Summation with Multi-Message Shuffling. CoRR (2019)
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
The power of synergy in differential privacy: Combining a small curator with local randomizers. CoRR (2019)
Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
Private Center Points and Learning of Halfspaces. COLT (2019)
Amos Beimel, Kobbi Nissim, Mohammad Zaheri
Exploring Differential Obliviousness. CoRR (2019)
Stefan Savage, Ian Levy, Kobbi Nissim, Paul Ohm, Carmela Troncoso, Nicole Wong
Surveillance and privacy in the public and private sectors: panel. CSLAW (2019)
Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols. FOCS (2018)
Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz
Segmentation, Incentives, and Privacy. Math. Oper. Res. 43 (4) (2018)
Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
The Limits of Post-Selection Generalization. NeurIPS (2018)
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
The Limits of Post-Selection Generalization. CoRR (2018)
Kobbi Nissim, Uri Stemmer
Clustering Algorithms for the Centralized and Local Models. ALT (2018)
Aloni Cohen, Kobbi Nissim
Linear Program Reconstruction in Practice. CoRR (2018)
Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak
Computational Two-Party Correlation. Electron. Colloquium Comput. Complex. 25 (2018)
Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz
Segmentation, Incentives and Privacy. CoRR (2018)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. CoRR (2017)
Kobbi Nissim, Uri Stemmer
Clustering Algorithms for the Centralized and Local Models. CoRR (2017)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
Practical Locally Private Heavy Hitters. NIPS (2017)
Kobbi Nissim, Uri Stemmer
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. CoRR (2017)
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin
Private Incremental Regression. CoRR (2017)
Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill
Accessing Data while Preserving Privacy. CoRR (2017)
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin
Private Incremental Regression. PODS (2017)
Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill
Generic Attacks on Secure Outsourced Databases. CCS (2016)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic stability for adaptive data analysis. STOC (2016)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. Theory Comput. 12 (1) (2016)
Michael J. Freedman, Carmit Hazay, Kobbi Nissim, Benny Pinkas
Efficient Set Intersection with Simulation-Based Security. J. Cryptol. 29 (1) (2016)
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
Adaptive Learning with Robust Generalization Guarantees. COLT (2016)
Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan R. Ullman, Salil P. Vadhan
PSI (Ψ): a Private data Sharing Interface. CoRR (2016)
Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
Locating a Small Cluster Privately. CoRR (2016)
Mark Bun, Kobbi Nissim, Uri Stemmer
Simultaneous Private Learning of Multiple Concepts. ITCS (2016)
Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith
Calibrating Noise to Sensitivity in Private Data Analysis. J. Priv. Confidentiality 7 (3) (2016)
Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
Locating a Small Cluster Privately. PODS (2016)
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
Adaptive Learning with Robust Generalization Guarantees. CoRR (2016)
Xianrui Meng, Seny Kamara, Kobbi Nissim, George Kollios
GRECS: Graph Encryption for Approximate Shortest Distance Queries. CCS (2015)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. CoRR (2015)
Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
Differentially Private Release and Learning of Threshold Functions. FOCS (2015)
Mark Bun, Kobbi Nissim, Uri Stemmer
Simultaneous Private Learning of Multiple Concepts. CoRR (2015)
Yiling Chen, Kobbi Nissim, Bo Waggoner
Fair Information Sharing for Treasure Hunting. AAAI (2015)
Xianrui Meng, Seny Kamara, Kobbi Nissim, George Kollios
GRECS: Graph Encryption for Approximate Shortest Distance Queries. IACR Cryptol. ePrint Arch. 2015 (2015)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Learning Privately with Labeled and Unlabeled Examples. SODA (2015)
Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
Differentially Private Release and Learning of Threshold Functions. CoRR (2015)
Kobbi Nissim, Salil P. Vadhan, David Xiao
Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals. CoRR (2014)
Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, Kobbi Nissim
Bounds on the sample complexity for private learning and private data release. Mach. Learn. 94 (3) (2014)
Kobbi Nissim, Salil P. Vadhan, David Xiao
Redrawing the boundaries on purchasing data from privacy-sensitive individuals. ITCS (2014)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Learning Privately with Labeled and Unlabeled Examples. CoRR (2014)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Characterizing the Sample Complexity of Private Learners. CoRR (2014)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. CoRR (2014)
Hai Brenner, Kobbi Nissim
Impossibility of Differentially Private Universally Optimal Mechanisms. SIAM J. Comput. 43 (5) (2014)
Krishnaram Kenthapadi, Nina Mishra, Kobbi Nissim
Denials leak information: Simulatable auditing. J. Comput. Syst. Sci. 79 (8) (2013)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Characterizing the sample complexity of private learners. ITCS (2013)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. APPROX-RANDOM (2013)
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
Analyzing Graphs with Node Differential Privacy. TCC (2013)
Alexander Kantor, Kobbi Nissim
Attacks on statistical databases: The highly noisy case. Inf. Process. Lett. 113 (12) (2013)
Yehuda Lindell, Kobbi Nissim, Claudio Orlandi
Hiding the Input-Size in Secure Two-Party Computation. ASIACRYPT (2) (2013)
Carmit Hazay, Kobbi Nissim
Efficient Set Operations in the Presence of Malicious Adversaries. J. Cryptol. 25 (3) (2012)
Yehuda Lindell, Kobbi Nissim, Claudio Orlandi
Hiding the Input-Size in Secure Two-Party Computation. IACR Cryptol. ePrint Arch. 2012 (2012)
Kobbi Nissim, Claudio Orlandi, Rann Smorodinsky
Privacy-aware mechanism design. EC (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Kobbi Nissim