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ORCIDKobbi Nissim
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- affiliation: Georgetown University, Washington, DC, USA
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2020 – today
- 2023
[i53]Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer:
On Differentially Private Online Predictions. CoRR abs/2302.14099 (2023)- [i52]Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan:
Private Everlasting Prediction. CoRR abs/2305.09579 (2023) - [i51]Kobbi Nissim, Uri Stemmer, Eliad Tsfadia:
Adaptive Data Analysis in a Balanced Adversarial Model. CoRR abs/2305.15452 (2023) - 2022
- [j31]Kobbi Nissim
, Chao Yan:
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model. J. Priv. Confidentiality 12(2) (2022) - [c63]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: 1671-1684 - 2021
- [j30]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Learning Privately with Labeled and Unlabeled Examples. Algorithmica 83(1): 177-215 (2021) - [j29]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) - [c62]Dmytro Bogatov, Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill:
εpsolute: Efficiently Querying Databases While Providing Differential Privacy. CCS 2021: 2262-2276 - [c61]Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer:
Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. CRYPTO (3) 2021: 94-121 - [c60]Kobbi Nissim:
Privacy: From Database Reconstruction to Legal Theorems. PODS 2021: 33-41 - [c59]Kobbi Nissim, Alexandra Wood:
Foundations for Robust Data Protection: Co-designing Law and Computer Science. TPS-ISA 2021: 235-242 - [e3]Kobbi Nissim, Brent Waters:
Theory of Cryptography - 19th International Conference, TCC 2021, Raleigh, NC, USA, November 8-11, 2021, Proceedings, Part I. Lecture Notes in Computer Science 13042, Springer 2021, ISBN 978-3-030-90458-6 [contents] - [e2]Kobbi Nissim, Brent Waters:
Theory of Cryptography - 19th International Conference, TCC 2021, Raleigh, NC, USA, November 8-11, 2021, Proceedings, Part II. Lecture Notes in Computer Science 13043, Springer 2021, ISBN 978-3-030-90452-4 [contents] - [e1]Kobbi Nissim, Brent Waters:
Theory of Cryptography - 19th International Conference, TCC 2021, Raleigh, NC, USA, November 8-11, 2021, Proceedings, Part III. Lecture Notes in Computer Science 13044, Springer 2021, ISBN 978-3-030-90455-5 [contents] - [i50]Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer:
Separating Adaptive Streaming from Oblivious Streaming. CoRR abs/2101.10836 (2021) - [i49]Kobbi Nissim, Chao Yan:
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model. CoRR abs/2103.15690 (2021) - [i48]Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak:
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols. CoRR abs/2105.00765 (2021) - [i47]Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer:
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds. CoRR abs/2111.03980 (2021) - 2020
- [j28]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21: 16:1-16:42 (2020) - [j27]Aloni Cohen, Kobbi Nissim:
Linear Program Reconstruction in Practice. J. Priv. Confidentiality 10(1) (2020) - [j26]Cynthia Dwork, Alan F. Karr, Kobbi Nissim, Lars Vilhuber:
On Privacy in the Age of COVID-19. J. Priv. Confidentiality 10(2) (2020) - [j25]Aloni Cohen, Kobbi Nissim:
Towards formalizing the GDPR's notion of singling out. Proc. Natl. Acad. Sci. USA 117(15): 8344-8352 (2020) - [j24]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): 1041-1082 (2020) - [c58]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
Private Summation in the Multi-Message Shuffle Model. CCS 2020: 657-676 - [c57]Marco Gaboardi, Kobbi Nissim, David Purser:
The Complexity of Verifying Loop-Free Programs as Differentially Private. ICALP 2020: 129:1-129:17 - [c56]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: 14:1-14:25 - [c55]Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer:
On the Round Complexity of the Shuffle Model. TCC (2) 2020: 683-712 - [i46]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
Private Summation in the Multi-Message Shuffle Model. CoRR abs/2002.00817 (2020) - [i45]Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer:
On the Round Complexity of the Shuffle Model. CoRR abs/2009.13510 (2020) - [i44]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 abs/2012.08571 (2020) - [i43]Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer:
On the Round Complexity of the Shuffle Model. IACR Cryptol. ePrint Arch. 2020: 1182 (2020)
2010 – 2019
- 2019
- [j23]Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. J. Mach. Learn. Res. 20: 94:1-94:34 (2019) - [j22]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the Sample Complexity of Pure Private Learners. J. Mach. Learn. Res. 20: 146:1-146:33 (2019) - [j21]Uri Stemmer, Kobbi Nissim:
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [c54]Amos Beimel, Kobbi Nissim, Mohammad Zaheri:
Exploring Differential Obliviousness. APPROX-RANDOM 2019: 65:1-65:20 - [c53]Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer:
Private Center Points and Learning of Halfspaces. COLT 2019: 269-282 - [c52]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
The Privacy Blanket of the Shuffle Model. CRYPTO (2) 2019: 638-667 - [c51]Stefan Savage, Ian Levy, Kobbi Nissim, Paul Ohm, Carmela Troncoso, Nicole Wong:
Surveillance and privacy in the public and private sectors: panel. CSLAW 2019: 1 - [i42]Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer:
Private Center Points and Learning of Halfspaces. CoRR abs/1902.10731 (2019) - [i41]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
The Privacy Blanket of the Shuffle Model. CoRR abs/1903.02837 (2019) - [i40]Aloni Cohen, Kobbi Nissim:
Towards Formalizing the GDPR's Notion of Singling Out. CoRR abs/1904.06009 (2019) - [i39]Amos Beimel, Kobbi Nissim, Mohammad Zaheri:
Exploring Differential Obliviousness. CoRR abs/1905.01373 (2019) - [i38]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
Differentially Private Summation with Multi-Message Shuffling. CoRR abs/1906.09116 (2019) - [i37]Borja Balle, James Bell, Adrià Gascón, Kobbi Nissim:
Improved Summation from Shuffling. CoRR abs/1909.11225 (2019) - [i36]Marco Gaboardi, Kobbi Nissim, David Purser:
The Complexity of Verifying Circuits as Differentially Private. CoRR abs/1911.03272 (2019) - [i35]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 abs/1912.08951 (2019) - 2018
- [j20]Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz:
Segmentation, Incentives, and Privacy. Math. Oper. Res. 43(4): 1252-1268 (2018) - [c50]Kobbi Nissim, Uri Stemmer:
Clustering Algorithms for the Centralized and Local Models. ALT 2018: 619-653 - [c49]Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak:
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols. FOCS 2018: 136-147 - [c48]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [i34]Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz:
Segmentation, Incentives and Privacy. CoRR abs/1806.00966 (2018) - [i33]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - [i32]Aloni Cohen, Kobbi Nissim:
Linear Program Reconstruction in Practice. CoRR abs/1810.05692 (2018) - [i31]Iftach Haitner, Kobbi Nissim, Eran Omri, Ronen Shaltiel, Jad Silbak:
Computational Two-Party Correlation. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [c47]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta:
Practical Locally Private Heavy Hitters. NIPS 2017: 2288-2296 - [c46]Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin:
Private Incremental Regression. PODS 2017: 167-182 - [i30]Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin:
Private Incremental Regression. CoRR abs/1701.01093 (2017) - [i29]Kobbi Nissim, Uri Stemmer:
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. CoRR abs/1703.01970 (2017) - [i28]Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill:
Accessing Data while Preserving Privacy. CoRR abs/1706.01552 (2017) - [i27]Kobbi Nissim, Uri Stemmer:
Clustering Algorithms for the Centralized and Local Models. CoRR abs/1707.04766 (2017) - [i26]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. CoRR abs/1707.04982 (2017) - 2016
- [j19]Michael J. Freedman, Carmit Hazay, Kobbi Nissim, Benny Pinkas:
Efficient Set Intersection with Simulation-Based Security. J. Cryptol. 29(1): 115-155 (2016) - [j18]Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith:
Calibrating Noise to Sensitivity in Private Data Analysis. J. Priv. Confidentiality 7(3): 17-51 (2016) - [j17]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. Theory Comput. 12(1): 1-61 (2016) - [c45]Georgios Kellaris, George Kollios, Kobbi Nissim, Adam O'Neill:
Generic Attacks on Secure Outsourced Databases. CCS 2016: 1329-1340 - [c44]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. COLT 2016: 772-814 - [c43]Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. ITCS 2016: 369-380 - [c42]Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Locating a Small Cluster Privately. PODS 2016: 413-427 - [c41]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [r1]Kobbi Nissim, David Xiao:
Mechanism Design and Differential Privacy. Encyclopedia of Algorithms 2016: 1247-1256 - [i25]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. CoRR abs/1602.07726 (2016) - [i24]Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Locating a Small Cluster Privately. CoRR abs/1604.05590 (2016) - [i23]Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan R. Ullman, Salil P. Vadhan:
PSI (Ψ): a Private data Sharing Interface. CoRR abs/1609.04340 (2016) - 2015
- [c40]Yiling Chen, Kobbi Nissim, Bo Waggoner:
Fair Information Sharing for Treasure Hunting. AAAI 2015: 851-857 - [c39]Xianrui Meng, Seny Kamara, Kobbi Nissim, George Kollios:
GRECS: Graph Encryption for Approximate Shortest Distance Queries. CCS 2015: 504-517 - [c38]Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Differentially Private Release and Learning of Threshold Functions. FOCS 2015: 634-649 - [c37]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Learning Privately with Labeled and Unlabeled Examples. SODA 2015: 461-477 - [i22]Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Differentially Private Release and Learning of Threshold Functions. CoRR abs/1504.07553 (2015) - [i21]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic Stability for Adaptive Data Analysis. CoRR abs/1511.02513 (2015) - [i20]Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. CoRR abs/1511.08552 (2015) - [i19]Xianrui Meng, Seny Kamara, Kobbi Nissim, George Kollios:
GRECS: Graph Encryption for Approximate Shortest Distance Queries. IACR Cryptol. ePrint Arch. 2015: 266 (2015) - 2014
- [j16]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): 401-437 (2014) - [j15]Hai Brenner, Kobbi Nissim:
Impossibility of Differentially Private Universally Optimal Mechanisms. SIAM J. Comput. 43(5): 1513-1540 (2014) - [c36]Kobbi Nissim, Salil P. Vadhan, David Xiao:
Redrawing the boundaries on purchasing data from privacy-sensitive individuals. ITCS 2014: 411-422 - [i18]Kobbi Nissim, Salil P. Vadhan, David Xiao:
Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals. CoRR abs/1401.4092 (2014) - [i17]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the Sample Complexity of Private Learners. CoRR abs/1402.2224 (2014) - [i16]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Learning Privately with Labeled and Unlabeled Examples. CoRR abs/1407.2662 (2014) - [i15]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. CoRR abs/1407.2674 (2014) - 2013
- [j14]Alexander Kantor, Kobbi Nissim:
Attacks on statistical databases: The highly noisy case. Inf. Process. Lett. 113(12): 409-413 (2013) - [j13]Krishnaram Kenthapadi, Nina Mishra, Kobbi Nissim:
Denials leak information: Simulatable auditing. J. Comput. Syst. Sci. 79(8): 1322-1340 (2013) - [c35]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. APPROX-RANDOM 2013: 363-378 - [c34]Yehuda Lindell, Kobbi Nissim, Claudio Orlandi:
Hiding the Input-Size in Secure Two-Party Computation. ASIACRYPT (2) 2013: 421-440 - [c33]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the sample complexity of private learners. ITCS 2013: 97-110 - [c32]Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
Analyzing Graphs with Node Differential Privacy. TCC 2013: 457-476 - 2012
- [j12]Carmit Hazay, Kobbi Nissim:
Efficient Set Operations in the Presence of Malicious Adversaries. J. Cryptol. 25(3): 383-433 (2012) - [c31]Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz:
Approximately optimal mechanism design via differential privacy. ITCS 2012: 203-213 - [c30]Kobbi Nissim, Claudio Orlandi, Rann Smorodinsky:
Privacy-aware mechanism design. EC 2012: 774-789 - [i14]Yehuda Lindell, Kobbi Nissim, Claudio Orlandi:
Hiding the Input-Size in Secure Two-Party Computation. IACR Cryptol. ePrint Arch. 2012: 679 (2012) - 2011
- [j11]Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
What Can We Learn Privately? SIAM J. Comput. 40(3): 793-826 (2011) - [i13]Amos Beimel, Kobbi Nissim, Eran Omri:
Distributed Private Data Analysis: On Simultaneously Solving How and What. CoRR abs/1103.2626 (2011) - [i12]Kobbi Nissim, Claudio Orlandi, Rann Smorodinsky:
Privacy-Aware Mechanism Design. CoRR abs/1111.3350 (2011) - 2010
- [j10]Amos Beimel, Tal Malkin, Kobbi Nissim, Enav Weinreb:
How Should We Solve Search Problems Privately? J. Cryptol. 23(2): 344-371 (2010) - [c29]Hai Brenner, Kobbi Nissim:
Impossibility of Differentially Private Universally Optimal Mechanisms. FOCS 2010: 71-80 - [c28]Carmit Hazay, Kobbi Nissim:
Efficient Set Operations in the Presence of Malicious Adversaries. Public Key Cryptography 2010: 312-331 - [c27]Amos Beimel, Shiva Prasad Kasiviswanathan, Kobbi Nissim:
Bounds on the Sample Complexity for Private Learning and Private Data Release. TCC 2010: 437-454 - [i11]Kobbi Nissim, Rann Smorodinsky, Moshe Tennenholtz:
Approximately Optimal Mechanism Design via Differential Privacy. CoRR abs/1004.2888 (2010) - [i10]Hai Brenner, Kobbi Nissim:
Impossibility of Differentially Private Universally Optimal Mechanisms. CoRR abs/1008.0256 (2010)
2000 – 2009
- 2009
- [j9]Amos Beimel, Renen Hallak, Kobbi Nissim:
Private Approximation of Clustering and Vertex Cover. Comput. Complex. 18(3): 435-494 (2009) - [j8]John M. Abowd, Kobbi Nissim, Chris J. Skinner:
First Issue Editorial. J. Priv. Confidentiality 1(1) (2009) - [c26]Dan Feldman, Amos Fiat, Haim Kaplan, Kobbi Nissim:
Private coresets. STOC 2009: 361-370 - [i9]Carmit Hazay, Kobbi Nissim:
Efficient Set Operations in the Presence of Malicious Adversaries. IACR Cryptol. ePrint Arch. 2009: 594 (2009) - 2008
- [j7]Amos Beimel, Paz Carmi, Kobbi Nissim, Enav Weinreb:
Private Approximation of Search Problems. SIAM J. Comput. 38(5): 1728-1760 (2008) - [c25]Amos Beimel, Kobbi Nissim, Eran Omri:
Distributed Private Data Analysis: Simultaneously Solving How and What. CRYPTO 2008: 451-468 - [c24]Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
What Can We Learn Privately? FOCS 2008: 531-540 - [p1]Kobbi Nissim:
Private Data Analysis via Output Perturbation - A Rigorous Approach to Constructing Sanitizers and Privacy Preserving Algorithms. Privacy-Preserving Data Mining 2008: 383-414 - [i8]Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
What Can We Learn Privately? CoRR abs/0803.0924 (2008) - 2007
- [j6]Prahladh Harsha, Yuval Ishai, Joe Kilian, Kobbi Nissim, Srinivasan Venkatesh:
Communication vs. Computation. Comput. Complex. 16(1): 1-33 (2007) - [c23]Amos Beimel, Tal Malkin, Kobbi Nissim, Enav Weinreb:
How Should We Solve Search Problems Privately? CRYPTO 2007: 31-49 - [c22]Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
Smooth sensitivity and sampling in private data analysis. STOC 2007: 75-84 - [c21]Amos Beimel, Renen Hallak, Kobbi Nissim:
Private Approximation of Clustering and Vertex Cover. TCC 2007: 383-403 - 2006
- [j5]Joan Feigenbaum, Yuval Ishai, Tal Malkin, Kobbi Nissim, Martin J. Strauss, Rebecca N. Wright:
Secure multiparty computation of approximations. ACM Trans. Algorithms 2(3): 435-472 (2006) - [c20]Ilya Mironov, Anton Mityagin, Kobbi Nissim:
Hard Instances of the Constrained Discrete Logarithm Problem. ANTS 2006: 582-598 - [c19]Amos Beimel, Paz Carmi, Kobbi Nissim, Enav Weinreb:
Private approximation of search problems. STOC 2006: 119-128 - [c18]Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith:
Calibrating Noise to Sensitivity in Private Data Analysis. TCC 2006: 265-284 - [c17]Kobbi Nissim, Enav Weinreb:
Communication Efficient Secure Linear Algebra. TCC 2006: 522-541 - [i7]Ilya Mironov, Anton Mityagin, Kobbi Nissim:
Hard Instances of the Constrained Discrete Logarithm Problem. CoRR abs/math/0606771 (2006) - [i6]Ilya Mironov, Anton Mityagin, Kobbi Nissim:
Hard Instances of the Constrained Discrete Logarithm Problem. IACR Cryptol. ePrint Arch. 2006: 253 (2006) - 2005
- [c16]Krishnaram Kenthapadi, Nina Mishra, Kobbi Nissim:
Simulatable auditing. PODS 2005: 118-127 - [c15]