Books

Daniel Kifer, Jörg Drechsler, Jerome Reiter and Aleksandra B Slavkovic, 2024, Handbook of Sharing Confidential Data: Differential Privacy, Secure Multiparty Computation, and Synthetic Data, CRC Press

Bee-Chung Chen, Daniel Kifer, Kristen LeFevre and Ashwin Machanavajjhala, 2009, Privacy-Preserving Data Publishing, NOW Publishers, pp. 1–167

Daniel Kifer, 2009, Encyclopedia of Database Systems, Springer US

Journal Articles

Daniel Kifer, Zeyu Ding, John Durrell, Prottay Protivash, Guanhong Wang, Yuxin Wang, Yingtai Xiao and Danfeng Zhang, 2025, "Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism", Journal of Privacy and Confidentiality

Daniel Kifer, Jiangtao Liu, Chaopeng Shen, Te Pei and Kathryn Lawson, 2025, "The value of terrain pattern, high-resolution data and ensemble modeling for landslide susceptibility prediction", Journal of Geophysical Research: Machine Learning and Computation

Daniel Kifer, John Abowd, Tamara Adams, Robert Ashmead, David Darais, Sourya Dey, Simson Garfinkel, Nathan Goldschlag, Michael Hawes, Philip Leclerc, Ethan Lew, Scott Moore, Rolando Rodríguez, Ramy Tadros and Lars Vilhuber, 2025, "A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census", Harvard Data Science Review

Daniel Kifer, Michail Skiadopoulos and Parisa Shokouhi, 2025, "A transfer learning approach to the prediction of porosity in additively manufactured metallic components", NDT & E International

Daniel Kifer, Michail Skiadopoulos, Evan P Bozek, Lalith Sai Srinivas Pillarisetti and Parisa Shokouhi, 2025, "A physics-informed clustering approach for ultrasonics-based nondestructive evaluation", NDT&E International (non-destructive testing and evaluation)

Daniel Kifer, Arjun Wilkins, Danfeng Zhang and Brian Karrer, 2024, "Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism", Journal of Privacy and Confidentiality, 14, (1)

Daniel Kifer, Prottay Protivash, John Durrell, Zeyu Ding and Danfeng Zhang, 2024, "Reconstruction Attacks on Aggressive Relaxations of Differential Privacy", Journal of Privacy and Confidentiality, 14, (3)

Daniel Kifer, Prabhav Borate, Jacques Rivière, Samson Marty, Chris Malone and Parisa Shokouhi, 2024, "Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments", Scientific Reports, 14

Daniel Kifer, Prabhav Borate, Jacques Rivière, Chris Marone, Ankur Mali and Parisa Shokouhi, 2023, "Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes", Nature communications

Daniel Kifer, Chaopeng Shen, Alison Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran Harman, Martyn Clark, Matthew Farthing, Dapeng Fang, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Yalan Song, Hylke Beck, Tadd Bindas, Dipankar Dwivedi, Kaui Fang, Marvin Höge, Chris Rackauckas, Binayak Mohanty, Tirthankar Roy, Chonggang Xu and Kathryn Lawson, 2023, "Differentiable modelling to unify machine learning and physical models for geosciences", Nature Reviews Earth & Environment

Daniel Kifer, Zeyu Ding, Yuxin Wang, Yingtai Xiao, Guanhong Wang and Danfeng Zhang, 2023, "Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms.", Journal of the VLDB

Daniel Kifer, Kuai Fang, Kathryn Lawson, Dapeng Feng and Chaopeng Shen, 2022, "The data synergy effects of time-series deep learning models in hydrology.", Water Resources Research, 58, (4)

Daniel Kifer, John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence and Pavel Zhuravlev, 2022, "The 2020 census disclosure avoidance system TopDown algorithm.", Harvard Data Science Review

Daniel Kifer and Alexander Ororbia, 2022, "The neural coding framework for learning generative models.", Nature Communications, 13, (1)

Daniel Kifer, Savinay Nagendra, Benjamin Mirus, Te Pei, Kathryn Lawson, Srikanth Banagere Manjunatha, Weixin Li, Hien Nguyen, Tong Qiu, Sarah Tran and Chaopeng Shen, 2022, "Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15

Daniel Kifer, Parisa Shokouhi, Vikas Kumar, Sumedha Prathipati, Seyyed Hosseini and Clyde Lee Giles, 2021, "Physics-informed deep learning for prediction of CO2 storage site response", Journal of Contaminant Hydrology, 241

Daniel Kifer, Parisa Shokouhi, Vrushali Girkar, Srisharan Shreedharan, Chris Malone and Clyde Lee Giles, 2021, "Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data", Geophysical Research Letters

Daniel Kifer, Alex Ororbia, Ankur Mali and Clyde L Giles, 2020, "Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations", IEEE Trans. Neural Networks Learn. Syst

Daniel Kifer, Kuai , Kathryn Lawson and Chaopeng Shen, 2020, "Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short-Term Memory Models for Soil Moisture Predictions", Water Resources Research

Daniel Kifer, Yue Wang and Jaewoo Lee, 2019, "Differentially Private Confidence Intervals for Empirical Risk Minimization", Journal of Privacy and Confidentiality, 9, (1)

Daniel Kifer, Corina Graif, Brittany Freelin, Yu-Hsuan Kuo, Hongjian Wang and Zhenhui Li, 2019, "Network Spillovers and Neighborhood Crime: A computational statistics analysis of employment-based networks of neighborhoods", Justice Quarterly

Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer and Zhenhui Li, 2019, "A Simple Baseline for Travel Time Estimation using Large-scale Trip Data", ACM Transactions on Intelligent Systems and Technology, 10, (2)

Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li and Wen-Ping Tsai, 2018, "HESS Opinions: Incubating Deep-learning-powered Hydrologic Science Advances as a Community", Hydrology and Earth System Sciences, 22, (11), pp. 5639-5656

Daniel Kifer, Yue Wang, Jaewoo Lee and Vishesh Karwa, 2018, "Statistical Approximating Distributions Under Differential Privacy", Journal of Privacy and Confidentiality

Daniel Kifer, Kuai Fang, Chaopeng Shen and Xiao Yang, 2017, "Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network", Geophysical Research Letters

Daniel Kifer, Alexander G. Ororbia II and C. Lee Giles, 2017, "Unifying Adversarial Training Algorithms with Data Gradient Regularization", Neural Computation

Daniel Kifer, Hongjian Wang, Corina Graif and Zhenhui Li, 2017, "Non-Stationary Model for Crime Rate Inference Using Modern Urban Data", IEEE Transactions on Big Data

Daniel Kifer, A. G. Ororbia II and C. L. Giles, 2016, "Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization", CoRR

Bing-Rong Lin and Daniel Kifer, 2015, "Information Measures in Statistical Privacy and Data Processing Applications", ACM Transactions on Knowledge Discovery from Data, 9, (4), pp. 28

Ashwin Machanavajjhala and Daniel Kifer, 2015, "Designing Statistical Privacy for Your Data", Communications of the ACM, 58, (3), pp. 58–67

Yue Wang, Jaewoo Lee and Daniel Kifer, 2015, "Differentially Private Hypothesis Testing, Revisited", CoRR, abs/1511.03376

Bing-Rong Lin and Daniel Kifer, 2014, "On Arbitrage-free Pricing for General Data Queries", PVLDB, 7, (9), pp. 757–768

Daniel Kifer and Ashwin Machanavajjhala, 2014, "Pufferfish: A Framework for Mathematical Privacy Definitions", ACM Transations on Database Systems, 39, (1), pp. 3

Daniel Kifer and Bing-Ron Lin, 2014, "Towards a Systematic Analysis of Privacy Definitions", Journal of Privacy and Confidentiality, 5, (2), pp. 57-109

Bing-Rong Lin and Daniel Kifer, 2012, "A Framework for Extracting Semantic Guarantees from Privacy", CoRR, abs/1208.5443

Daniel Kifer and B.-R. Lin, 2010, "Towards and Axiomatization of Statistical Privacy and Utility"

Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke and Muthuramakrishnan Venkitasubramaniam, 2007, "L-diversity: Privacy beyond k-anonymity", TKDD, 1, (1)

David J. Martin, Daniel Kifer, Ashwin Machanavajjhala, Johannes Gehrke and Joseph Y. Halpern, 2007, "Worst-Case Background Knowledge for Privacy-Preserving Data Publishing", CoRR, abs/0705.2787

Manuel Calimlim, Jim Cordes, Alan J. Demers, Julia Deneva, Johannes Gehrke, Daniel Kifer, Mirek Riedewald and Jayavel Shanmugasundaram, 2004, "A Vision for PetaByte Data Management and Analyis Services for theArecibo Telescope", IEEE Data Engineering Bulletin, 27, (4), pp. 12–20

Manuel Calimlim, Jim Cordes, Alan J. Demers, Julia Deneva, Johannes Gehrke, Daniel Kifer, Mirek Riedewald and Jayavel Shanmugasundaram, 2004, "A Vision for PetaByte Data Management and Analyis Services for the Arecibo Telescope", IEEE Data Eng. Bull., 27, (4), pp. 12–20

Cristian Bucila, Johannes Gehrke, Daniel Kifer and Walker M. White, 2003, "DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints", Data Mining and Knowledge Discovery, 7, (3), pp. 241–272

Conference Proceedings

Daniel Kifer, Neisarg Dave, Clyde Lee Giles and Ankur Mali, 2025, "Bridging Neural and Symbolic Computation: A Learnability Study of RNNs on Counter and Dyck Languages", 19th Conference on Neurosymbolic Learning and Reasoning

Daniel Kifer, Abdolmehdi Behroozi and Chaopeng Shen, 2025, "Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations", International Conference on Learning Representations (ICLR)

Daniel Kifer, Yingtai Xiao, Jian Du, Shikun Zhang, Wanrong Zhang, Qian Yang and Danfeng Zhang, 2025, "Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy", IEEE Symposium on Security and Privacy (S&P), pp. 2919-2937

Daniel Kifer and Savinay Nagendra, 2024, "PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization", IEEE/CVF Winter Conference on Applications of Computer Vision

Daniel Kifer, Brett Mullins, Miguel Fuentes, Yingtai Xiao, Cameron Musco and Daniel Sheldon, 2024, "Efficient and Private Marginal Reconstruction with Local Non-Negativity", Advances in Neural Information Processing Systems 37 (NeurIPS 2024)

Daniel Kifer, Ron Jarmin, John Abowd, Robert Ashmead, Ryan Cumings-Menon, Nathan Goldschlag, Michael Hawes, Sallie Keller, Philip Leclerc, Jerome Reiter, Rolanda Rodruiguez, Ian Schmutte, Victoria Velkoff and Pavel Zhuravlev, 2023, "An in-depth examination of requirements for disclosure risk assessment", Proceedings of the National Academy of Sciences, 120, (43)

Daniel Kifer, Alexander G Ororbia, Ankur Mali and Clyde L Giles, 2023, "Backpropagation-Free Deep Learning with Recursive Local Representation Alignment", AAAI, 37, (8)

Daniel Kifer, Yingtai Xaio, Guanhong Wang and Danfeng Zhang, 2023, "Answering Private Linear Queries Adaptively using the Common Mechanism", Proceedings of the VLDB, 16, (8), pp. 1883-1896

Daniel Kifer, Yingtai Xiao, Guanlin He and Danfeng Zhang, 2023, "An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions"

Daniel Kifer, Ankur Arjun Mali, Alexander G Ororbia and Clyde L Giles, 2022, ". Neural JPEG: end-to-end image compression leveraging a standard JPEG encoder-decoder", Digital Compression Conference (DCC)

Daniel Kifer, 2021, "OmniLayout: Room Layout Reconstruction From Indoor Spherical Panoramas."

Daniel Kifer, Ankur Arjun Mali, Alex Ororbia and Clyde Lee Giles, 2021, "Recognizing Long Grammatical Sequences using Recurrent Networks Augmented with an External Differentiable Stack"

Daniel Kifer, Ankur Arjun Mali, Alex Ororbia and Clyde Lee Giles, 2021, "Investigating Backpropagation Alternatives when Learning to Dynamically Count with Recurrent Neural Networks"

Daniel Kifer, John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task and Pavel Zhuravlev, 2021, "An Uncertainty Principle is a Price of Privacy-Preserving Microdata"

Daniel Kifer, Ankur Mali, Alex Ororbia and Clyde L Giles, 2021, "Recognizing and Verifying Mathematical Equations Using Multiplicative Differential Neural Units"

Daniel Kifer, Ankur Mali, Alex Ororbia and Clyde L Giles, 2021, "An Empirical Analysis of Recurrent Learning Algorithms in Neural Lossy Image Compression Systems"

Daniel Kifer and Jaewoo Lee, 2021, "Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping"

Daniel Kifer, Yingtai Xiao, Zeyu Ding, Yuxin Wang and Danfeng Zhang, 2021, "Optimizing Fitness-For-Use of Differentially Private Linear Queries"

Daniel Kifer, Yuxin Wang, Zeyu Ding, Yingtai Xiao and Danfeng Zhang, 2021, "DPGen: Automated Program Synthesis for Differential Privacy"

Daniel Kifer, Yuxin Wang, Zeyu Ding and Danfeng Zhang, 2020, "CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples"

Daniel Kifer, Ke Yuan, Dafang He, Xiao Yang, Zhi Tang and Clyde L Giles, 2020, "Follow The Curve: Arbitrarily Oriented Scene Text Detection Using Key Points Spotting And Curve Prediction."

Daniel Kifer, Zeyu Ding, Yuxin Wang and Danfeng Zhang, 2019, "Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms", 13, (3), pp. 293-306

Daniel Kifer, Anand Gopalakrishnan, Ankur Mali, Lee Giles and Alexander Ororbia, 2019, "A Neural Temporal Model for Human Motion Prediction"

Daniel Kifer, Yuxin Wang, Zeyu Ding, Guanhong Wang and Danfeng Zhang, 2019, "Proving Differential Privacy with Shadow Execution"

Daniel Kifer, Xiao Yang, Madian Khabsa, Miaosen Wang, Wei Wang, Ahmed Hassan Awadallah and Lee Giles, 2019, "Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching"

Daniel Kifer, Dafang He, Xiao Yang and C. Lee Giles, 2019, "TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade"

Daniel Kifer, Chen Chen and Jaewoo Lee, 2019, "Renyi Differentially Private ERM for Smooth Objectives", pp. 2037-2046

Daniel Kifer, Songshan Yang, Jiawei Wen and Xiang Zhan, 2019, "ET-Lasso: A New Efficient Tuning of Lasso for High-Dimensional Data"

Jaewoo Lee and Daniel Kifer, 2018, "Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget", pp. 1656-1665

Yu-Hsuan Kuo, Zhenhui Li and Daniel Kifer, 2018, "Detecting Outliers in Data with Correlated Measures", pp. 287-296

Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay and Ashwin Machanavajjhala, 2018, "Differentially Private Hierarchical Count-of-Counts Histograms", 11, (11), pp. 1509-1521

Daniel Kifer, Zeyu Ding, Yuxin Wang, Guanhong Wang and Danfeng Zhang, 2018, "Detecting Violations of Differential Privacy", ACM, pp. 475-489

Daniel Kifer and Omar Montasser, 2017, "Predicting Demographics of High-Resolution Geographies with Geotagged Tweets"

Daniel Kifer and Ryan Rogers, 2017, "A New Class of Private Chi-Square Hypothesis Tests", pp. 991-1000

Daniel Kifer, H. Dafang, X. Yang, C. Liang, Z. Zhou, Alexander G. Ororbia II, and C. Lee Giles, 2017, "Multi-scale FCN with Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild"

Daniel Kifer, Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley and C. Lee Giles, 2017, "Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks"

Daniel Kifer, Xiao Yang, Dafang He, Zihan Zhou and C. Lee Giles, 2017, "Improving Offline Handwritten Chinese Character Recognition by Iterative Refinement"

Daniel Kifer, Dafang He, Scott Cohen, Brian L. Price and C. Lee Giles, 2017, "Multi-Scale MultiTask FCN for Semantic Page Segmentation and Table Detection"

Daniel Kifer, Xiao Yang, Dafang He, Zihan Zhou and C. Lee Giles, 2017, "Learning to Read Irregular Text with Attention Mechanisms"

Daniel Kifer, Xiao Yang, Dafang He, Wenyi Huang, Alexander Ororbia, Zihan Zhou and C. Lee Giles, 2017, "Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading"

Daniel Kifer and Danfeng Zhang, 2017, "LightDP: Towards Automating Differential Privacy Proofs"

Daniel Kifer, H. Wang, Y. H. Kuo and Z. Li, 2016, "A Simple Baseline for Travel Time Estimation Using Large-scale Trip Data", ACM

Daniel Kifer, W. Huang, D. He, X. Yang, Z. Zhou and C. L. Giles, 2016, "Detecting Arbitrary Oriented Text in the Wild with a Visual Attention Model", ACM, pp. 551-555

Daniel Kifer, H. Wang, C. Graif and Z. Li, 2016, "Crime Rate Inference with Big Data", ACM, 13, pp. 635-644

Jaewoo Lee, Yue Wang and Daniel Kifer, 2015, "Maximum Likelihood Postprocessing for Differential Privacy under Consistency Constraints", pp. 635–644

Daniel Kifer, 2015, "On Estimating the Swapping Rate for Categorical Data", pp. 557–566

Daniel Kifer, 2015, "Privacy and the Price of Data", pp. 16

Bing-Rong Lin and Daniel Kifer, 2013, "Geometry of privacy and utility", pp. 281–284

Sirinda Palahan, Domagoj Babic, Swarat Chaudhuri and Daniel Kifer, 2013, "Extraction of statistically significant malware behaviors", pp. 69–78

Bing-Rong Lin and Daniel Kifer, 2013, "Information Preservation in Statistical Privacy and Bayesian Estimationof Unattributed Histograms", SIGMOD, New York, NY, pp. 677–688

Daniel Kifer and B.-R. Lin, 2012, "Analyzing Privacy and Utility Using Axioms", Proceedings of the Forty-Sixth Asilomar Conference on Signals, Systems and Computers

Daniel Kifer, 2012, "COLT 2012 - The 25th Annual Conference on Learning Theory", JMLR.org, 23

Daniel Kifer, Adam D Smith and Abhradeep Thakurta, 2012, "Private Convex Optimization for Empirical Risk Minimization with Applicationsto High-dimensional Regression", pp. 25.1–25.40

Daniel Kifer and Ashwin Machanavajjhala, 2012, "A rigorous and customizable framework for privacy", pp. 77–88

, 2012, "Proceedings of the 31st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2012, Scottsdale, AZ, USA, May 20-24, 2012", ACM

Bing-Rong Lin and Daniel Kifer, 2012, "Reasoning about privacy using axioms", pp. 975–979

, 2012, "Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers, ACSCC 2012, Pacific Grove, CA, USA, November 4-7, 2012", IEEE

Daniel Kifer, Adam D Smith and Abhradeep Thakurta, 2012, "Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression", pp. 25.1–25.40

Daniel Kifer and Ashwin Machanavajjhala, 2011, "No free lunch in data privacy", pp. 193–204

Qi He, Daniel Kifer, Jian Pei, Prasenjit Mitra and Clyde L Giles, 2011, "Citation recommendation without author supervision", pp. 755–764

Bi Chen, Leilei Zhu, Daniel Kifer and Dongwon Lee, 2010, "What Is an Opinion About? Exploring Political Standpoints Using OpinionScoring Model"

Daniel Kifer and Bing-Rong Lin, 2010, "Towards an axiomatization of statistical privacy and utility", pp. 147–158

Qi He, Jian Pei, Daniel Kifer, Prasenjit Mitra and Clyde L Giles, 2010, "Context-aware citation recommendation", pp. 421–430

Johannes Gehrke, Daniel Kifer and Ashwin Machanavajjhala, 2010, "Privacy in data publishing", pp. 1213

Bi Chen, Leilei Zhu, Daniel Kifer and Dongwon Lee, 2010, "What Is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model"

Daniel Kifer, 2009, "Attacks on privacy and deFinetti’s theorem", pp. 127–138

, 2009, "Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, Providence, Rhode Island, USA, June 29 - July 2, 2009", ACM

Parag Agrawal, Daniel Kifer and Christopher Olston, 2008, "Scheduling shared scans of large data files", 1, (1), pp. 958–969

Ashwin Machanavajjhala, Daniel Kifer, John M. Abowd, Johannes Gehrke and Lars Vilhuber, 2008, "Privacy: Theory meets Practice on the Map", pp. 277–286

, 2008, "Proceedings of the 24th International Conference on Data Engineering, ICDE 2008, April 7-12, 2008, Cancún, México", IEEE

David J. Martin, Daniel Kifer, Ashwin Machanavajjhala, Johannes Gehrke and Joseph Y. Halpern, 2007, "Worst-Case Background Knowledge for Privacy-Preserving Data Publishing", pp. 126–135

, 2007, "Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15-20, 2007", IEEE

Daniel Kifer and Johannes Gehrke, 2006, "Injecting utility into anonymized datasets", pp. 217–228

Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer and Muthuramakrishnan Venkitasubramaniam, 2006, "l-Diversity: Privacy Beyond k-Anonymity", pp. 24

, 2006, "Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, 3-8 April 2006, Atlanta, GA, USA", IEEE Computer Society

Daniel Kifer, Shai Ben-David and Johannes Gehrke, 2004, "Detecting Change in Data Streams", pp. 180–191

Daniel Kifer, Johannes Gehrke, Cristian Bucila and Walker M. White, 2003, "How to quickly find a witness", pp. 272–283

Cristian Bucila, Johannes Gehrke, Daniel Kifer and Walker M. White, 2002, "DualMiner: a dual-pruning algorithm for itemsets with constraints", pp. 42–51

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