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ORCIDDaniel Kifer
Dan Kifer
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- affiliation: Pennsylvania State University, University Park, USA
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2020 – today
- 2022
[i47]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles:
An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems. CoRR abs/2201.11782 (2022)- [i46]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles:
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder. CoRR abs/2201.11795 (2022) - [i45]Brian Karrer, Daniel Kifer, Arjun Wilkins, Danfeng Zhang:
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism. CoRR abs/2202.01100 (2022) - [i44]John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Daniel Kifer, Philip Leclerc, Jeffrey Ocker, Michael Ratcliffe, Pavel Zhuravlev:
Geographic Spines in the 2020 Census Disclosure Avoidance System TopDown Algorithm. CoRR abs/2203.16654 (2022) - [i43]John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson L. Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence, Pavel Zhuravlev:
The 2020 Census Disclosure Avoidance System TopDown Algorithm. CoRR abs/2204.08986 (2022) - 2021
- [j23]Jaewoo Lee, Daniel Kifer:
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping. Proc. Priv. Enhancing Technol. 2021(1): 128-144 (2021) - [j22]Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer:
Optimizing Fitness-For-Use of Differentially Private Linear Queries. Proc. VLDB Endow. 14(10): 1730-1742 (2021) - [c57]Ankur Arjun Mali, Alexander G. Ororbia II, Daniel Kifer, C. Lee Giles:
Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units. AAAI 2021: 5006-5015 - [c56]Yuxin Wang, Zeyu Ding, Yingtai Xiao, Daniel Kifer, Danfeng Zhang:
DPGen: Automated Program Synthesis for Differential Privacy. CCS 2021: 393-411 - [c55]Shivansh Rao, Vikas Kumar, Daniel Kifer, C. Lee Giles, Ankur Arjun Mali:
OmniLayout: Room Layout Reconstruction From Indoor Spherical Panoramas. CVPR Workshops 2021: 3706-3715 - [c54]Ankur Arjun Mali, Alexander G. Ororbia II, Dan Kifer, C. Lee Giles:
An Empirical Analysis of Recurrent Learning Algorithms in Neural Lossy Image Compression Systems. DCC 2021: 356 - [c53]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles:
Recognizing Long Grammatical Sequences using Recurrent Networks Augmented with an External Differentiable Stack. ICGI 2021: 130-153 - [c52]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles:
Investigating Backpropagation Alternatives when Learning to Dynamically Count with Recurrent Neural Networks. ICGI 2021: 154-175 - [c51]John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson L. Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev:
An Uncertainty Principle is a Price of Privacy-Preserving Microdata. NeurIPS 2021: 11883-11895 - [i42]Kuai Fang, Daniel Kifer, Kathryn Lawson
, Dapeng Feng, Chaopeng Shen:
The data synergy effects of time-series deep learning models in hydrology. CoRR abs/2101.01876 (2021) - [i41]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, C. Lee Giles:
Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units. CoRR abs/2104.02899 (2021) - [i40]Shivansh Rao, Vikas Kumar, Daniel Kifer, C. Lee Giles, Ankur Arjun Mali:
OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas. CoRR abs/2104.09403 (2021) - [i39]Zeyu Ding, Daniel Kifer, Sayed M. Saghaian N. E., Thomas Steinke, Yuxin Wang, Yingtai Xiao, Danfeng Zhang:
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise. CoRR abs/2105.07260 (2021) - [i38]Yuxin Wang, Zeyu Ding, Yingtai Xiao, Daniel Kifer, Danfeng Zhang:
DPGen: Automated Program Synthesis for Differential Privacy. CoRR abs/2109.07441 (2021) - [i37]John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson L. Garfinkel, Daniel Kifer, Philip Leclerc, William Sexton, Ashley Simpson, Christine Task, Pavel Zhuravlev:
An Uncertainty Principle is a Price of Privacy-Preserving Microdata. CoRR abs/2110.13239 (2021) - 2020
- [j21]Alexander Ororbia, Ankur Arjun Mali, C. Lee Giles, Daniel Kifer:
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations. IEEE Trans. Neural Networks Learn. Syst. 31(10): 4267-4278 (2020) - [c50]Yuxin Wang, Zeyu Ding, Daniel Kifer, Danfeng Zhang:
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples. CCS 2020: 919-938 - [c49]Ke Yuan, Dafang He, Xiao Yang, Zhi Tang, Daniel Kifer, C. Lee Giles:
Follow The Curve: Arbitrarily Oriented Scene Text Detection Using Key Points Spotting And Curve Prediction. ICME 2020: 1-6 - [i36]Alexander Ororbia, Ankur Arjun Mali, Daniel Kifer, C. Lee Giles:
Reducing the Computational Burden of Deep Learning with Recursive Local Representation Alignment. CoRR abs/2002.03911 (2020) - [i35]Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang:
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR abs/2002.04049 (2020) - [i34]Ankur Arjun Mali, Alexander Ororbia, Daniel Kifer, Clyde Lee Giles:
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack. CoRR abs/2004.07623 (2020) - [i33]Yuxin Wang, Zeyu Ding, Daniel Kifer, Danfeng Zhang:
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples. CoRR abs/2008.07485 (2020) - [i32]Jaewoo Lee, Daniel Kifer:
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping. CoRR abs/2009.03106 (2020) - [i31]Jaewoo Lee, Daniel Kifer:
Differentially Private Deep Learning with Direct Feedback Alignment. CoRR abs/2010.03701 (2020) - [i30]Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer:
Optimizing Fitness-For-Use of Differentially Private Linear Queries. CoRR abs/2012.00135 (2020) - [i29]Zeyu Ding, Yuxin Wang, Yingtai Xiao, Guanhong Wang, Danfeng Zhang, Daniel Kifer:
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms. CoRR abs/2012.01592 (2020) - [i28]Alexander Ororbia, Daniel Kifer:
The Neural Coding Framework for Learning Generative Models. CoRR abs/2012.03405 (2020)
2010 – 2019
- 2019
- [j20]Yue Wang, Daniel Kifer, Jaewoo Lee:
Differentially Private Confidence Intervals for Empirical Risk Minimization. J. Priv. Confidentiality 9(1) (2019) - [j19]Zeyu Ding, Yuxin Wang, Danfeng Zhang, Dan Kifer:
Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms. Proc. VLDB Endow. 13(3): 293-306 (2019) - [j18]Hongjian Wang, Huaxiu Yao, Daniel Kifer, Corina Graif, Zhenhui Li:
Non-Stationary Model for Crime Rate Inference Using Modern Urban Data. IEEE Trans. Big Data 5(2): 180-194 (2019) - [j17]Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, Zhenhui Li:
A Simple Baseline for Travel Time Estimation using Large-scale Trip Data. ACM Trans. Intell. Syst. Technol. 10(2): 19:1-19:22 (2019) - [c48]Xiao Yang, Madian Khabsa, Miaosen Wang, Wei Wang, Ahmed Hassan Awadallah, Daniel Kifer, C. Lee Giles:
Adversarial Training for Community Question Answer Selection Based on Multi-Scale Matching. AAAI 2019: 395-402 - [c47]Chen Chen, Jaewoo Lee, Dan Kifer:
Renyi Differentially Private ERM for Smooth Objectives. AISTATS 2019: 2037-2046 - [c46]Xiao Yang, Dafang He, Daniel Kifer, C. Lee Giles:
A Learning-based Text Synthesis Engine for Scene Text Detection. BMVC 2019: 94 - [c45]Anand Gopalakrishnan, Ankur Arjun Mali, Dan Kifer, C. Lee Giles, Alexander G. Ororbia II:
A Neural Temporal Model for Human Motion Prediction. CVPR 2019: 12116-12125 - [c44]Songshan Yang, Jiawei Wen, Xiang Zhan, Daniel Kifer:
ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data. KDD 2019: 607-616 - [c43]Yuxin Wang, Zeyu Ding, Guanhong Wang, Daniel Kifer, Danfeng Zhang:
Proving differential privacy with shadow execution. PLDI 2019: 655-669 - [c42]Dafang He, Xiao Yang, Dan Kifer, C. Lee Giles:
TextContourNet: A Flexible and Effective Framework for Improving Scene Text Detection Architecture With a Multi-Task Cascade. WACV 2019: 676-685 - [i27]Yuxin Wang, Zeyu Ding, Guanhong Wang, Daniel Kifer, Danfeng Zhang:
Proving Differential Privacy with Shadow Execution. CoRR abs/1903.12254 (2019) - [i26]Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer:
Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms. CoRR abs/1904.12773 (2019) - [i25]Alexander Ororbia, Ankur Arjun Mali, Daniel Kifer, C. Lee Giles:
Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting when Learning Cumulatively. CoRR abs/1905.10696 (2019) - [i24]Kuai Fang, Chaopeng Shen, Daniel Kifer:
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions. CoRR abs/1906.04595 (2019) - 2018
- [j16]Dan Kifer:
Reminiscenses of Steve Fienberg. J. Priv. Confidentiality 8(1) (2018) - [j15]Yue Wang, Daniel Kifer, Jaewoo Lee, Vishesh Karwa:
Statistical Approximating Distributions Under Differential Privacy. J. Priv. Confidentiality 8(1) (2018) - [j14]Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala:
Differentially Private Hierarchical Count-of-Counts Histograms. Proc. VLDB Endow. 11(11): 1509-1521 (2018) - [c41]Dafang He, Yeqing Li, Alexander N. Gorban, Derrall Heath, Julian Ibarz, Qian Yu, Daniel Kifer, C. Lee Giles:
Large Scale Scene Text Verification with Guided Attention. ACCV (5) 2018: 260-275 - [c40]Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, Daniel Kifer:
Detecting Violations of Differential Privacy. CCS 2018: 475-489 - [c39]Yu-Hsuan Kuo, Zhenhui Li, Daniel Kifer:
Detecting Outliers in Data with Correlated Measures. CIKM 2018: 287-296 - [c38]Jaewoo Lee, Daniel Kifer:
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget. KDD 2018: 1656-1665 - [r2]Daniel Kifer:
Change Detection on Streams. Encyclopedia of Database Systems (2nd ed.) 2018 - [i23]Alexander G. Ororbia II, Ankur Arjun Mali, Daniel Kifer, C. Lee Giles:
Conducting Credit Assignment by Aligning Local Representations. CoRR abs/1803.01834 (2018) - [i22]Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala:
Differentially Private Hierarchical Group Size Estimation. CoRR abs/1804.00370 (2018) - [i21]Yue Wang, Daniel Kifer, Jaewoo Lee:
Differentially Private Confidence Intervals for Empirical Risk Minimization. CoRR abs/1804.03794 (2018) - [i20]Dafang He, Yeqing Li, Alexander N. Gorban, Derrall Heath, Julian Ibarz, Qian Yu, Daniel Kifer, C. Lee Giles:
Guided Attention for Large Scale Scene Text Verification. CoRR abs/1804.08588 (2018) - [i19]Ding Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, Daniel Kifer:
Toward Detecting Violations of Differential Privacy. CoRR abs/1805.10277 (2018) - [i18]Yu-Hsuan Kuo, Zhenhui Li, Daniel Kifer:
Detecting Outliers in Data with Correlated Measures. CoRR abs/1808.08640 (2018) - [i17]Jaewoo Lee, Daniel Kifer:
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget. CoRR abs/1808.09501 (2018) - [i16]Anand Gopalakrishnan, Ankur Arjun Mali, Dan Kifer, C. Lee Giles, Alexander G. Ororbia II:
A Neural Temporal Model for Human Motion Prediction. CoRR abs/1809.03036 (2018) - [i15]Dafang He, Xiao Yang, Daniel Kifer, C. Lee Giles:
TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade. CoRR abs/1809.03050 (2018) - [i14]Songshan Yang, Jiawei Wen, Xiang Zhan, Daniel Kifer:
ET-Lasso: Efficient Tuning of Lasso for High-Dimensional Data. CoRR abs/1810.04513 (2018) - [i13]Alexander Ororbia, Ankur Arjun Mali, C. Lee Giles, Daniel Kifer:
Online Learning of Recurrent Neural Architectures by Locally Aligning Distributed Representations. CoRR abs/1810.07411 (2018) - 2017
- [j13]Alexander G. Ororbia II, Daniel Kifer, C. Lee Giles:
Unifying Adversarial Training Algorithms with Data Gradient Regularization. Neural Comput. 29(4): 867-887 (2017) - [c37]Omar Montasser, Daniel Kifer:
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. AAAI 2017: 1460-1466 - [c36]Ryan Rogers, Daniel Kifer:
A New Class of Private Chi-Square Hypothesis Tests. AISTATS 2017: 991-1000 - [c35]Dafang He, Xiao Yang, Chen Liang, Zihan Zhou, Alexander G. Ororbia II, Daniel Kifer, C. Lee Giles:
Multi-scale FCN with Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild. CVPR 2017: 474-483 - [c34]Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles:
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks. CVPR 2017: 4342-4351 - [c33]Xiao Yang, Dafang He, Zihan Zhou, Daniel Kifer, C. Lee Giles:
Improving Offline Handwritten Chinese Character Recognition by Iterative Refinement. ICDAR 2017: 5-10 - [c32]Dafang He, Scott Cohen, Brian L. Price, Daniel Kifer, C. Lee Giles:
Multi-Scale Multi-Task FCN for Semantic Page Segmentation and Table Detection. ICDAR 2017: 254-261 - [c31]Xiao Yang, Dafang He, Zihan Zhou, Daniel Kifer, C. Lee Giles:
Learning to Read Irregular Text with Attention Mechanisms. IJCAI 2017: 3280-3286 - [c30]Xiao Yang, Dafang He, Wenyi Huang, Alexander Ororbia, Zihan Zhou, Daniel Kifer, C. Lee Giles:
Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading. JCDL 2017: 245-248 - [c29]Danfeng Zhang, Daniel Kifer:
LightDP: towards automating differential privacy proofs. POPL 2017: 888-901 - [i12]Omar Montasser, Daniel Kifer:
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. CoRR abs/1701.06225 (2017) - [i11]Xiao Yang, Mehmet Ersin Yümer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles:
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network. CoRR abs/1706.02337 (2017) - 2016
- [c28]Dafang He, Xiao Yang, Wenyi Huang, Zihan Zhou, Daniel Kifer, C. Lee Giles:
Aggregating Local Context for Accurate Scene Text Detection. ACCV (5) 2016: 280-296 - [c27]Hongjian Wang, Yu-Hsuan Kuo, Daniel Kifer, Zhenhui Li:
A simple baseline for travel time estimation using large-scale trip data. SIGSPATIAL/GIS 2016: 61:1-61:4 - [c26]Hongjian Wang, Daniel Kifer, Corina Graif, Zhenhui Li:
Crime Rate Inference with Big Data. KDD 2016: 635-644 - [c25]Wenyi Huang, Dafang He, Xiao Yang, Zihan Zhou, Daniel Kifer, C. Lee Giles:
Detecting Arbitrary Oriented Text in the Wild with a Visual Attention Model. ACM Multimedia 2016: 551-555 - [i10]Alexander G. Ororbia II, C. Lee Giles, Daniel Kifer:
Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization. CoRR abs/1601.07213 (2016) - [i9]Danfeng Zhang, Daniel Kifer:
AutoPriv: Automating Differential Privacy Proofs. CoRR abs/1607.08228 (2016) - [i8]Jaewoo Lee, Daniel Kifer:
Postprocessing for Iterative Differentially Private Algorithms. CoRR abs/1609.03251 (2016) - [i7]Daniel Kifer, Ryan Rogers:
A New Class of Private Chi-Square Tests. CoRR abs/1610.07662 (2016) - [i6]Xiao Yang, Dafang He, Wenyi Huang, Zihan Zhou, Alexander Ororbia, Dan Kifer, C. Lee Giles:
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading. CoRR abs/1611.07385 (2016) - 2015
- [j12]Ashwin Machanavajjhala, Daniel Kifer:
Designing statistical privacy for your data. Commun. ACM 58(3): 58-67 (2015) - [j11]Bing-Rong Lin, Daniel Kifer:
Information Measures in Statistical Privacy and Data Processing Applications. ACM Trans. Knowl. Discov. Data 9(4): 28:1-28:29 (2015) - [c24]Daniel Kifer:
On Estimating the Swapping Rate for Categorical Data. KDD 2015: 557-566 - [c23]Jaewoo Lee, Yue Wang, Daniel Kifer:
Maximum Likelihood Postprocessing for Differential Privacy under Consistency Constraints. KDD 2015: 635-644 - [c22]Daniel Kifer:
Privacy and the Price of Data. LICS 2015: 16 - [i5]Yue Wang, Jaewoo Lee, Daniel Kifer:
Differentially Private Hypothesis Testing, Revisited. CoRR abs/1511.03376 (2015) - [i4]Vishesh Karwa, Dan Kifer, Aleksandra B. Slavkovic:
Private Posterior distributions from Variational approximations. CoRR abs/1511.07896 (2015) - [i3]Hongjian Wang, Zhenhui Li, Yu-Hsuan Kuo, Dan Kifer:
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data. CoRR abs/1512.08580 (2015) - 2014
- [j10]Bing-Rong Lin, Dan Kifer:
Towards a Systematic Analysis of Privacy Definitions. J. Priv. Confidentiality 5(2) (2014) - [j9]Bing-Rong Lin, Daniel Kifer:
On Arbitrage-free Pricing for General Data Queries. Proc. VLDB Endow. 7(9): 757-768 (2014) - [j8]Daniel Kifer, Ashwin Machanavajjhala:
Pufferfish: A framework for mathematical privacy definitions. ACM Trans. Database Syst. 39(1): 3:1-3:36 (2014) - 2013
- [j7]Dan Kifer:
Introduction to Special Section. J. Priv. Confidentiality 5(1) (2013) - [c21]Sirinda Palahan, Domagoj Babic, Swarat Chaudhuri, Daniel Kifer:
Extraction of statistically significant malware behaviors. ACSAC 2013: 69-78 - [c20]Bing-Rong Lin, Daniel Kifer:
Geometry of privacy and utility. GlobalSIP 2013: 281-284 - [c19]Bing-Rong Lin, Daniel Kifer:
Information preservation in statistical privacy and bayesian estimation of unattributed histograms. SIGMOD Conference 2013: 677-688 - 2012
- [j6]Daniel Kifer, Bing-Rong Lin:
An Axiomatic View of Statistical Privacy and Utility. J. Priv. Confidentiality 4(1) (2012) - [c18]Bing-Rong Lin, Daniel Kifer:
Reasoning about privacy using axioms. ACSCC 2012: 975-979 - [c17]Daniel Kifer, Ashwin Machanavajjhala:
A rigorous and customizable framework for privacy. PODS 2012: 77-88 - [c16]Daniel Kifer, Adam D. Smith, Abhradeep Thakurta:
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. COLT 2012: 25.1-25.40 - [i2]Bing-Rong Lin, Daniel Kifer:
A Framework for Extracting Semantic Guarantees from Privacy. CoRR abs/1208.5443 (2012) - 2011
- [c15]Daniel Kifer, Ashwin Machanavajjhala:
No free lunch in data privacy. SIGMOD Conference 2011: 193-204 - [c14]Qi He, Daniel Kifer, Jian Pei, Prasenjit Mitra, C. Lee Giles:
Citation recommendation without author supervision. WSDM 2011: 755-764 - 2010
- [c13]Bi Chen, Leilei Zhu, Daniel Kifer, Dongwon Lee:
What Is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model. AAAI 2010 - [c12]Johannes Gehrke, Daniel Kifer, Ashwin Machanavajjhala:
Privacy in data publishing. ICDE 2010: 1213 - [c11]Daniel Kifer, Bing-Rong Lin:
Towards an axiomatization of statistical privacy and utility. PODS 2010: 147-158 - [c10]Qi He, Jian Pei, Daniel Kifer, Prasenjit Mitra, C. Lee Giles:
Context-aware citation recommendation. WWW 2010: 421-430
2000 – 2009
- 2009
- [j5]Bee-Chung Chen, Daniel Kifer, Kristen LeFevre, Ashwin Machanavajjhala:
Privacy-Preserving Data Publishing. Found. Trends Databases 2(1-2): 1-167 (2009) - [c9]Daniel Kifer:
Attacks on privacy and deFinetti's theorem. SIGMOD Conference 2009: 127-138 - [r1]Daniel Kifer:
Change Detection on Streams. Encyclopedia of Database Systems 2009: 317-321 - 2008
- [j4]Parag Agrawal, Daniel Kifer, Christopher Olston:
Scheduling shared scans of large data files. Proc. VLDB Endow. 1(1): 958-969 (2008) - [c8]Ashwin Machanavajjhala, Daniel Kifer, John M. Abowd, Johannes Gehrke, Lars Vilhuber:
Privacy: Theory meets Practice on the Map. ICDE 2008: 277-286 - 2007
- [j3]Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, Muthuramakrishnan Venkitasubramaniam:
L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1): 3 (2007) - [c7]David J. Martin, Daniel Kifer, Ashwin Machanavajjhala, Johannes Gehrke, Joseph Y. Halpern:
Worst-Case Background Knowledge for Privacy-Preserving Data Publishing. ICDE 2007: 126-135 - [i1]David J. Martin, Daniel Kifer, Ashwin Machanavajjhala, Johannes Gehrke, Joseph Y. Halpern:
Worst-Case Background Knowledge for Privacy-Preserving Data Publishing. CoRR abs/0705.2787 (2007) - 2006
- [b1]Dan Kifer:
Graphs and Privacy. Cornell University, USA, 2006 - [c6]