Uri Stemmer - RankMe

135 papers A* 38A 11B 1Journal 78Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	A	`conf`	A Simple and Robust Protocol for Distributed Counting. ITCS Edith Cohen, Moshe Shechner, Uri Stemmer
2026	J	`jnl`	Adaptively Robust Resettable Streaming. CoRR Edith Cohen, Elena Gribelyuk, Jelani Nelson, Uri Stemmer
2026	A	`conf`	Bayesian Perspective on Memorization and Reconstruction. ITCS Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
2026	A*	`conf`	One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches. SODA Edith Cohen, Jelani Nelson, Tamás Sarlós, Mihir Singhal, Uri Stemmer
2026	J	`jnl`	Protecting the Undeleted in Machine Unlearning. CoRR Aloni Cohen, Refael Kohen, Kobbi Nissim, Uri Stemmer
2025	J	`jnl`	A Simple and Robust Protocol for Distributed Counting. CoRR Edith Cohen, Moshe Shechner, Uri Stemmer
2025	J	`jnl`	Bayesian Perspective on Memorization and Reconstruction. CoRR Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
2025	A*	`conf`	Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries. ICML Edith Cohen, Mihir Singhal, Uri Stemmer
2025	J	`jnl`	Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries. CoRR Edith Cohen, Mihir Singhal, Uri Stemmer
2025	A	`conf`	Data Reconstruction: When You See It and When You Don't. ITCS Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
2025	A*	`conf`	Minimizing Recourse in an Adaptive Balls and Bins Game. ICALP Adi Fine, Haim Kaplan, Uri Stemmer
2025	A*	`conf`	Nearly Optimal Sample Complexity for Learning with Label Proportions. ICML Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer
2025	J	`jnl`	Nearly Optimal Sample Complexity for Learning with Label Proportions. CoRR Róbert Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer
2025	A*	`conf`	On Differentially Private Linear Algebra. STOC Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur
2025	J	`jnl`	The Cost of Compression: Tight Quadratic Black-Box Attacks on Sketches for ℓ CoRR Sara Ahmadian, Edith Cohen, Uri Stemmer
2025	J	`jnl`	Tight Bounds for Answering Adaptively Chosen Concentrated Queries. CoRR Emma Rapoport, Edith Cohen, Uri Stemmer
2024	J	`jnl`	A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators. Algorithmica Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer
2024	J	`jnl`	Data Reconstruction: When You See It and When You Don't. CoRR Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
2024	A*	`conf`	Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. COLT Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2024	J	`jnl`	Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. CoRR Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2024	J	`jnl`	Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. IACR Cryptol. ePrint Arch. Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2024	—	`conf`	MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CRYPTO (8) Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
2024	J	`jnl`	On Differentially Private Linear Algebra. CoRR Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur
2024	J	`jnl`	One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches. CoRR Edith Cohen, Jelani Nelson, Tamás Sarlós, Mihir Singhal, Uri Stemmer
2024	A*	`conf`	Private Truly-Everlasting Robust-Prediction. ICML Uri Stemmer
2024	J	`jnl`	Private Truly-Everlasting Robust-Prediction. CoRR Uri Stemmer
2023	A	`conf`	A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. ITCS Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer
2023	A*	`conf`	Adaptive Data Analysis in a Balanced Adversarial Model. NeurIPS Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
2023	J	`jnl`	Adaptive Data Analysis in a Balanced Adversarial Model. CoRR Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
2023	A*	`conf`	Black-Box Differential Privacy for Interactive ML. NeurIPS Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
2023	A*	`conf`	Concurrent Shuffle Differential Privacy Under Continual Observation. ICML Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
2023	J	`jnl`	Concurrent Shuffle Differential Privacy Under Continual Observation. CoRR Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
2023	A	`conf`	Generalized Private Selection and Testing with High Confidence. ITCS Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2023	J	`jnl`	Hot PATE: Private Aggregation of Distributions for Diverse Task. CoRR Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2023	—	`conf`	On Differential Privacy and Adaptive Data Analysis with Bounded Space. EUROCRYPT (3) Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
2023	J	`jnl`	On Differential Privacy and Adaptive Data Analysis with Bounded Space. CoRR Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
2023	J	`jnl`	On Differential Privacy and Adaptive Data Analysis with Bounded Space. IACR Cryptol. ePrint Arch. Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
2023	J	`jnl`	On Differentially Private Online Predictions. CoRR Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
2023	A*	`conf`	Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. STOC Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2023	A*	`conf`	Private Everlasting Prediction. NeurIPS Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
2023	J	`jnl`	Private Everlasting Prediction. CoRR Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
2023	J	`jnl`	Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model. CoRR Menachem Sadigurschi, Moshe Shechner, Uri Stemmer
2023	A	`conf`	Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model. ESA Menachem Sadigurschi, Moshe Shechner, Uri Stemmer
2023	A*	`conf`	Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. AAAI Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2022	A*	`conf`	Adaptive Data Analysis with Correlated Observations. ICML Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
2022	J	`jnl`	Adaptive Data Analysis with Correlated Observations. CoRR Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
2022	J	`jnl`	Adversarially Robust Streaming Algorithms via Differential Privacy. J. ACM Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2022	A*	`conf`	Differentially Private Approximate Quantiles. ICML Haim Kaplan, Shachar Schnapp, Uri Stemmer
2022	J	`jnl`	Differentially Private Learning of Geometric Concepts. SIAM J. Comput. Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2022	J	`jnl`	Differentially-Private Bayes Consistency. CoRR Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
2022	A*	`conf`	Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. STOC Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
2022	A*	`conf`	FriendlyCore: Practical Differentially Private Aggregation. ICML Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer
2022	J	`jnl`	Generalized Private Selection and Testing with High Confidence. CoRR Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2022	J	`jnl`	MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CoRR Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
2022	J	`jnl`	MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. IACR Cryptol. ePrint Arch. Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
2022	A*	`conf`	Monotone Learning. COLT Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
2022	J	`jnl`	Monotone Learning. CoRR Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
2022	A*	`conf`	On the Robustness of CountSketch to Adaptive Inputs. ICML Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer
2022	J	`jnl`	On the Robustness of CountSketch to Adaptive Inputs. CoRR Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer
2022	J	`jnl`	Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. CoRR Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2022	J	`jnl`	Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. CoRR Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
2021	J	`jnl`	A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer
2021	J	`jnl`	A Note on Sanitizing Streams with Differential Privacy. CoRR Haim Kaplan, Uri Stemmer
2021	J	`jnl`	Algorithmic Stability for Adaptive Data Analysis. SIAM J. Comput. Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
2021	J	`jnl`	Differentially Private Approximate Quantiles. CoRR Haim Kaplan, Shachar Schnapp, Uri Stemmer
2021	A*	`conf`	Differentially Private Multi-Armed Bandits in the Shuffle Model. NeurIPS Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
2021	J	`jnl`	Differentially Private Multi-Armed Bandits in the Shuffle Model. CoRR Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
2021	A	`conf`	Differentially Private Weighted Sampling. AISTATS Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer
2021	A*	`conf`	Differentially-Private Clustering of Easy Instances. ICML Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
2021	J	`jnl`	Differentially-Private Clustering of Easy Instances. CoRR Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
2021	J	`jnl`	Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds. CoRR Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
2021	J	`jnl`	FriendlyCore: Practical Differentially Private Aggregation. CoRR Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer
2021	J	`jnl`	Learning Privately with Labeled and Unlabeled Examples. Algorithmica Amos Beimel, Kobbi Nissim, Uri Stemmer
2021	—	`conf`	Learning and Evaluating a Differentially Private Pre-trained Language Model. EMNLP (Findings) Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias
2021	J	`jnl`	Locally Private k-Means Clustering. J. Mach. Learn. Res. Uri Stemmer
2021	A*	`conf`	On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. NeurIPS Menachem Sadigurschi, Uri Stemmer
2021	J	`jnl`	On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. CoRR Menachem Sadigurschi, Uri Stemmer
2021	—	`conf`	Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. CRYPTO (3) Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
2021	J	`jnl`	Separating Adaptive Streaming from Oblivious Streaming. CoRR Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
2021	A*	`conf`	The Sparse Vector Technique, Revisited. COLT Haim Kaplan, Yishay Mansour, Uri Stemmer
2020	A*	`conf`	Adversarially Robust Streaming Algorithms via Differential Privacy. NeurIPS Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2020	J	`jnl`	Adversarially Robust Streaming Algorithms via Differential Privacy. CoRR Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2020	A*	`conf`	Closure Properties for Private Classification and Online Prediction. COLT Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer
2020	J	`jnl`	Closure Properties for Private Classification and Online Prediction. CoRR Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer
2020	J	`jnl`	Differentially Private Weighted Sampling. CoRR Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer
2020	A*	`conf`	How to Find a Point in the Convex Hull Privately. SoCG Haim Kaplan, Micha Sharir, Uri Stemmer
2020	J	`jnl`	How to Find a Point in the Convex Hull Privately. CoRR Haim Kaplan, Micha Sharir, Uri Stemmer
2020	A*	`conf`	Locally Private SODA Uri Stemmer
2020	—	`conf`	On the Round Complexity of the Shuffle Model. TCC (2) Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
2020	J	`jnl`	On the Round Complexity of the Shuffle Model. CoRR Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
2020	J	`jnl`	On the Round Complexity of the Shuffle Model. IACR Cryptol. ePrint Arch. Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
2020	J	`jnl`	Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
2020	A*	`conf`	Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. NeurIPS Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
2020	J	`jnl`	Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. CoRR Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
2020	A	`conf`	Private k-Means Clustering with Stability Assumptions. AISTATS Moshe Shechner, Or Sheffet, Uri Stemmer
2020	A*	`conf`	Privately Learning Thresholds: Closing the Exponential Gap. COLT Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer
2020	A	`conf`	The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. ITC Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
2020	J	`jnl`	The Sparse Vector Technique, Revisited. CoRR Haim Kaplan, Yishay Mansour, Uri Stemmer
2019	J	`jnl`	Characterizing the Sample Complexity of Pure Private Learners. J. Mach. Learn. Res. Amos Beimel, Kobbi Nissim, Uri Stemmer
2019	J	`jnl`	Concentration Bounds for High Sensitivity Functions Through Differential Privacy. J. Priv. Confidentiality Uri Stemmer, Kobbi Nissim
2019	A*	`conf`	Differentially Private Learning of Geometric Concepts. ICML Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2019	J	`jnl`	Differentially Private Learning of Geometric Concepts. CoRR Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2019	J	`jnl`	Heavy Hitters and the Structure of Local Privacy. ACM Trans. Algorithms Mark Bun, Jelani Nelson, Uri Stemmer
2019	J	`jnl`	Locally Private k-Means Clustering. CoRR Uri Stemmer
2019	A*	`conf`	Private Center Points and Learning of Halfspaces. COLT Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
2019	J	`jnl`	Private Center Points and Learning of Halfspaces. CoRR Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
2019	J	`jnl`	Privately Learning Thresholds: Closing the Exponential Gap. CoRR Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer
2019	J	`jnl`	Simultaneous Private Learning of Multiple Concepts. J. Mach. Learn. Res. Mark Bun, Kobbi Nissim, Uri Stemmer
2019	J	`jnl`	The power of synergy in differential privacy: Combining a small curator with local randomizers. CoRR Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
2018	B	`conf`	Clustering Algorithms for the Centralized and Local Models. ALT Kobbi Nissim, Uri Stemmer
2018	A*	`conf`	Differentially Private k-Means with Constant Multiplicative Error. NeurIPS Uri Stemmer, Haim Kaplan
2018	J	`jnl`	Differentially Private k-Means with Constant Multiplicative Error. CoRR Haim Kaplan, Uri Stemmer
2018	A*	`conf`	Heavy Hitters and the Structure of Local Privacy. PODS Mark Bun, Jelani Nelson, Uri Stemmer
2018	A*	`conf`	The Limits of Post-Selection Generalization. NeurIPS Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
2018	J	`jnl`	The Limits of Post-Selection Generalization. CoRR Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
2017	J	`jnl`	Clustering Algorithms for the Centralized and Local Models. CoRR Kobbi Nissim, Uri Stemmer
2017	J	`jnl`	Concentration Bounds for High Sensitivity Functions Through Differential Privacy. CoRR Kobbi Nissim, Uri Stemmer
2017	J	`jnl`	Heavy Hitters and the Structure of Local Privacy. CoRR Mark Bun, Jelani Nelson, Uri Stemmer
2017	—	`conf`	Practical Locally Private Heavy Hitters. NIPS Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
2017	J	`jnl`	Practical Locally Private Heavy Hitters. CoRR Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
2016	A*	`conf`	Algorithmic stability for adaptive data analysis. STOC Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
2016	A*	`conf`	Locating a Small Cluster Privately. PODS Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
2016	J	`jnl`	Locating a Small Cluster Privately. CoRR Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
2016	J	`jnl`	Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. Theory Comput. Amos Beimel, Kobbi Nissim, Uri Stemmer
2016	A	`conf`	Simultaneous Private Learning of Multiple Concepts. ITCS Mark Bun, Kobbi Nissim, Uri Stemmer
2015	J	`jnl`	Algorithmic Stability for Adaptive Data Analysis. CoRR Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
2015	A*	`conf`	Differentially Private Release and Learning of Threshold Functions. FOCS Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
2015	J	`jnl`	Differentially Private Release and Learning of Threshold Functions. CoRR Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan
2015	A*	`conf`	Learning Privately with Labeled and Unlabeled Examples. SODA Amos Beimel, Kobbi Nissim, Uri Stemmer
2015	J	`jnl`	Simultaneous Private Learning of Multiple Concepts. CoRR Mark Bun, Kobbi Nissim, Uri Stemmer
2014	J	`jnl`	Characterizing the Sample Complexity of Private Learners. CoRR Amos Beimel, Kobbi Nissim, Uri Stemmer
2014	J	`jnl`	Learning Privately with Labeled and Unlabeled Examples. CoRR Amos Beimel, Kobbi Nissim, Uri Stemmer
2014	J	`jnl`	Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. CoRR Amos Beimel, Kobbi Nissim, Uri Stemmer
2013	A	`conf`	Characterizing the sample complexity of private learners. ITCS Amos Beimel, Kobbi Nissim, Uri Stemmer
2013	—	`conf`	Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. APPROX-RANDOM Amos Beimel, Kobbi Nissim, Uri Stemmer