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ORCIDAshwin Machanavajjhala
M. V. N. Ashwin Kumar
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- affiliation: Duke University, Durham, USA
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2020 – today
- 2024
[j44]Jeremy Seeman, William Sexton, David Pujol, Ashwin Machanavajjhala:
Privately Answering Queries on Skewed Data via Per-Record Differential Privacy. Proc. VLDB Endow. 17(11): 3138-3150 (2024)- [j43]Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala:
Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys. ACM Trans. Database Syst. 49(4): 13:1-13:40 (2024) - [i48]Brian Finley, Anthony M. Caruso, Justin C. Doty, Ashwin Machanavajjhala, Mikaela R. Meyer, David Pujol, William Sexton, Zachary Terner:
Slowly Scaling Per-Record Differential Privacy. CoRR abs/2409.18118 (2024) - 2023
- [j42]Ryan McKenna, Gerome Miklau
, Michael Hay, Ashwin Machanavajjhala:
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality 13(1) (2023) - [j41]David Pujol, Amir Gilad, Ashwin Machanavajjhala:
PreFair: Privately Generating Justifiably Fair Synthetic Data. Proc. VLDB Endow. 16(6): 1573-1586 (2023) - [j40]Yanping Zhang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala:
Longshot: Indexing Growing Databases using MPC and Differential Privacy. Proc. VLDB Endow. 16(8): 2005-2018 (2023) - [j39]Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
Explaining Differentially Private Query Results With DPXPlain. Proc. VLDB Endow. 16(12): 3962-3965 (2023) - [j38]Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. Proc. VLDB Endow. 17(1): 65-78 (2023) - [j37]Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala:
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys. SIGMOD Rec. 52(1): 115-123 (2023) - [c63]Chenghong Wang, David Pujol, Kartik Nayak, Ashwin Machanavajjhala:
Private Proof-of-Stake Blockchains using Differentially-Private Stake Distortion. USENIX Security Symposium 2023: 1577-1594 - [i47]Temilola Adeleye, Skye Berghel, Damien Desfontaines, Michael Hay, Isaac Johnson, Cléo Lemoisson, Ashwin Machanavajjhala, Tom Magerlein, Gabriele Modena, David Pujol, Daniel Simmons-Marengo, Hal Triedman:
Publishing Wikipedia usage data with strong privacy guarantees. CoRR abs/2308.16298 (2023) - [i46]Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. CoRR abs/2309.08574 (2023) - [i45]Jeremy Seeman, William Sexton, David Pujol, Ashwin Machanavajjhala:
Privately Answering Queries on Skewed Data via Per Record Differential Privacy. CoRR abs/2310.12827 (2023) - [i44]Chenghong Wang, David Pujol, Kartik Nayak, Ashwin Machanavajjhala:
Private Proof-of-Stake Blockchains using Differentially-private Stake Distortion. IACR Cryptol. ePrint Arch. 2023: 787 (2023) - 2022
- [j36]Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DPXPlain: Privately Explaining Aggregate Query Answers. Proc. VLDB Endow. 16(1): 113-126 (2022) - [j35]David Pujol, Albert Sun, Brandon Fain, Ashwin Machanavajjhala:
Multi-Analyst Differential Privacy for Online Query Answering. Proc. VLDB Endow. 16(4): 816-828 (2022) - [c62]Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala:
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys. SIGMOD Conference 2022: 759-772 - [c61]Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala:
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. SIGMOD Conference 2022: 818-832 - [i43]Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala:
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. CoRR abs/2203.05084 (2022) - [i42]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) - [i41]Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy:
DPXPlain: Privately Explaining Aggregate Query Answers. CoRR abs/2209.01286 (2022) - [i40]Daniel Kifer, John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Philip Leclerc, Ashwin Machanavajjhala, William Sexton, Pavel Zhuravlev:
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. CoRR abs/2209.03310 (2022) - [i39]Skye Berghel, Philip Bohannon, Damien Desfontaines, Charles Estes, Samuel Haney, Luke Hartman, Michael Hay, Ashwin Machanavajjhala, Tom Magerlein, Gerome Miklau, Amritha Pai, William Sexton, Ruchit Shrestha:
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy. CoRR abs/2212.04133 (2022) - [i38]David Pujol, Albert Sun, Brandon Fain, Ashwin Machanavajjhala:
Multi-Analyst Differential Privacy for Online Query Answering. CoRR abs/2212.09884 (2022) - [i37]David Pujol, Amir Gilad, Ashwin Machanavajjhala:
PreFair: Privately Generating Justifiably Fair Synthetic Data. CoRR abs/2212.10310 (2022) - 2021
- [j34]Sameer Wagh, Xi He, Ashwin Machanavajjhala, Prateek Mittal:
DP-cryptography: marrying differential privacy and cryptography in emerging applications. Commun. ACM 64(2): 84-93 (2021) - [j33]David Pujol, Ashwin Machanavajjhala:
Equity and Privacy: More Than Just a Tradeoff. IEEE Secur. Priv. 19(6): 93-97 (2021) - [j32]David Pujol, Yikai Wu, Brandon Fain, Ashwin Machanavajjhala:
Budget Sharing for Multi-Analyst Differential Privacy. Proc. VLDB Endow. 14(10): 1805-1817 (2021) - [c60]Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala:
Synthesizing Linked Data Under Cardinality and Integrity Constraints. SIGMOD Conference 2021: 619-631 - [c59]Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala:
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy. SIGMOD Conference 2021: 1892-1905 - [c58]Xi He, Jennie Rogers, Johes Bater, Ashwin Machanavajjhala, Chenghong Wang, Xiao Wang:
Practical Security and Privacy for Database Systems. SIGMOD Conference 2021: 2839-2845 - [i36]Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala:
Synthesizing Linked Data Under Cardinality and Integrity Constraints. CoRR abs/2103.14435 (2021) - [i35]Chenghong Wang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala:
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy. CoRR abs/2103.15942 (2021) - [i34]Yuchao Tao, Johes Bater, Ashwin Machanavajjhala:
Prior-Aware Distribution Estimation for Differential Privacy. CoRR abs/2106.05131 (2021) - [i33]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/2106.12118 (2021) - [i32]Samuel Haney, William Sexton, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity. CoRR abs/2107.10659 (2021) - [i31]David Pujol, Ashwin Machanavajjhala:
Equity and Privacy: More Than Just a Tradeoff. CoRR abs/2111.04671 (2021) - [i30]Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR abs/2112.09238 (2021) - 2020
- [j31]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
ϵKTELO: A Framework for Defining Differentially Private Computations. ACM Trans. Database Syst. 45(1): 2:1-2:44 (2020) - [c57]David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair decision making using privacy-protected data. FAT* 2020: 189-199 - [c56]Ios Kotsogiannis, Stelios Doudalis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra:
One-sided Differential Privacy. ICDE 2020: 493-504 - [c55]Yanping Zhang, Chenghong Wang, David Pujol, Johes Bater, Matthew Lentz, Ashwin Machanavajjhala, Kartik Nayak, Lavanya Vasudevan, Jun Yang:
Poirot: private contact summary aggregation: poster abstract. SenSys 2020: 774-775 - [c54]Yuchao Tao, Xi He, Ashwin Machanavajjhala, Sudeepa Roy:
Computing Local Sensitivities of Counting Queries with Joins. SIGMOD Conference 2020: 479-494 - [c53]Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha:
Crypt?: Crypto-Assisted Differential Privacy on Untrusted Servers. SIGMOD Conference 2020: 603-619 - [i29]Yuchao Tao, Xi He, Ashwin Machanavajjhala, Sudeepa Roy:
Computing Local Sensitivities of Counting Queries with Joins. CoRR abs/2004.04656 (2020) - [i28]Sameer Wagh, Xi He, Ashwin Machanavajjhala, Prateek Mittal:
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications. CoRR abs/2004.08887 (2020) - [i27]David Pujol, Yikai Wu, Brandon Fain, Ashwin Machanavajjhala:
Budget Sharing for Multi-Analyst Differential Privacy. CoRR abs/2011.01192 (2020)
2010 – 2019
- 2019
- [j30]Andrew David Foote, Ashwin Machanavajjhala, Kevin McKinney:
Releasing Earnings Distributions using Differential Privacy - Disclosure Avoidance System For Post-Secondary Employment Outcomes (PSEO). J. Priv. Confidentiality 9(2) (2019) - [j29]Nisarg Raval, Ashwin Machanavajjhala, Jerry Pan:
Olympus: Sensor Privacy through Utility Aware Obfuscation. Proc. Priv. Enhancing Technol. 2019(1): 5-25 (2019) - [j28]Ios Kotsogiannis, Yuchao Tao, Xi He, Maryam Fanaeepour, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
PrivateSQL: A Differentially Private SQL Query Engine. Proc. VLDB Endow. 12(11): 1371-1384 (2019) - [j27]Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. 12(12): 1918-1921 (2019) - [j26]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
#8712;: A Framework for Defining Differentially-Private Computations. SIGMOD Rec. 48(1): 15-22 (2019) - [c52]Ios Kotsogiannis, Yuchao Tao, Ashwin Machanavajjhala, Gerome Miklau, Michael Hay:
Architecting a Differentially Private SQL Engine. CIDR 2019 - [c51]Maryam Fanaeepour, Ashwin Machanavajjhala:
PrivStream: Differentially Private Event Detection on Data Streams. CODASPY 2019: 145-147 - [c50]Nisarg Raval, Ali Razeen, Ashwin Machanavajjhala, Landon P. Cox, Andrew Warfield:
Permissions Plugins as Android Apps. MobiSys 2019: 180-192 - [c49]Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. NeurIPS 2019: 3469-3478 - [c48]Chang Ge, Xi He, Ihab F. Ilyas, Ashwin Machanavajjhala:
APEx: Accuracy-Aware Differentially Private Data Exploration. SIGMOD Conference 2019: 177-194 - [c47]Jennie Rogers, Johes Bater, Xi He, Ashwin Machanavajjhala, Madhav Suresh, Xiao Wang:
Privacy Changes Everything. Poly/DMAH@VLDB 2019: 96-111 - [i26]Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha:
Outis: Crypto-Assisted Differential Privacy on Untrusted Servers. CoRR abs/1902.07756 (2019) - [i25]Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair Decision Making using Privacy-Protected Data. CoRR abs/1905.12744 (2019) - [i24]Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. CoRR abs/1907.02159 (2019) - [i23]Yikai Wu, David Pujol, Ios Kotsogiannis, Ashwin Machanavajjhala:
Answering Summation Queries for Numerical Attributes under Differential Privacy. CoRR abs/1908.10268 (2019) - 2018
- [j25]Yan Chen, Andrés F. Barrientos, Ashwin Machanavajjhala, Jerome P. Reiter:
Is my model any good: differentially private regression diagnostics. Knowl. Inf. Syst. 54(1): 33-64 (2018) - [j24]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. 11(10): 1206-1219 (2018) - [j23]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) - [j22]Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, Jennie Rogers:
ShrinkWrap: Efficient SQL Query Processing in Differentially Private Data Federations. Proc. VLDB Endow. 12(3): 307-320 (2018) - [c46]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference 2018: 115-130 - [c45]Sameera Ghayyur, Yan Chen, Roberto Yus, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau, Sharad Mehrotra:
IoT-Detective: Analyzing IoT Data Under Differential Privacy. SIGMOD Conference 2018: 1725-1728 - [r4]Ashwin Machanavajjhala, Johannes Gehrke:
Randomization Methods to Ensure Data Privacy. Encyclopedia of Database Systems (2nd ed.) 2018 - [r3]Ashwin Machanavajjhala, Xi He:
Analyzing Your Location Data with Provable Privacy Guarantees. Handbook of Mobile Data Privacy 2018: 97-127 - [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]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/1808.03537 (2018) - [i20]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Ektelo: A Framework for Defining Differentially-Private Computations. CoRR abs/1808.03555 (2018) - [i19]Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, Jennie Rogers:
Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. CoRR abs/1810.01816 (2018) - 2017
- [c44]Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
PeGaSus: Data-Adaptive Differentially Private Stream Processing. CCS 2017: 1375-1388 - [c43]Xi He, Ashwin Machanavajjhala, Cheryl J. Flynn, Divesh Srivastava:
Composing Differential Privacy and Secure Computation: A Case Study on Scaling Private Record Linkage. CCS 2017: 1389-1406 - [c42]Nisarg Raval, Ashwin Machanavajjhala, Landon P. Cox:
Protecting Visual Secrets Using Adversarial Nets. CVPR Workshops 2017: 1329-1332 - [c41]Christopher Streiffer, Animesh Srivastava, Victor Orlikowski, Yesenia Velasco, Vincentius Martin, Nisarg Raval, Ashwin Machanavajjhala, Landon P. Cox:
ePrivateeye: to the edge and beyond! SEC 2017: 18:1-18:13 - [c40]Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau:
Pythia: Data Dependent Differentially Private Algorithm Selection. SIGMOD Conference 2017: 1323-1337 - [c39]Samuel Haney, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, Mark Kutzbach, Lars Vilhuber:
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics. SIGMOD Conference 2017: 1339-1354 - [c38]Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Margaret Orr:
DIAS: Differentially Private Interactive Algorithm Selection using Pythia. SIGMOD Conference 2017: 1679-1682 - [c37]Ashwin Machanavajjhala, Xi He, Michael Hay:
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges. SIGMOD Conference 2017: 1727-1730 - [c36]Ios Kotsogiannis, Elena Zheleva, Ashwin Machanavajjhala:
Directed Edge Recommender System. WSDM 2017: 525-533 - [i18]John M. Abowd, Lorenzo Alvisi, Cynthia Dwork, Sampath Kannan, Ashwin Machanavajjhala, Jerome P. Reiter:
Privacy-Preserving Data Analysis for the Federal Statistical Agencies. CoRR abs/1701.00752 (2017) - [i17]Xi He, Ashwin Machanavajjhala, Cheryl J. Flynn, Divesh Srivastava:
Scaling Private Record Linkage using Output Constrained Differential Privacy. CoRR abs/1702.00535 (2017) - [i16]Stelios Doudalis, Ios Kotsogiannis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra:
One-sided Differential Privacy. CoRR abs/1712.05888 (2017) - [i15]Chang Ge, Ihab F. Ilyas, Xi He, Ashwin Machanavajjhala:
Private Exploration Primitives for Data Cleaning. CoRR abs/1712.10266 (2017) - 2016
- [j21]Xi He, Nisarg Raval, Ashwin Machanavajjhala:
A Demonstration of VisDPT: Visual Exploration of Differentially Private Trajectories. Proc. VLDB Endow. 9(13): 1489-1492 (2016) - [j20]Ashwin Machanavajjhala, Xi He, Michael Hay:
Differential Privacy in the Wild: A tutorial on current practices & open challenges. Proc. VLDB Endow. 9(13): 1611-1614 (2016) - [c35]Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, Andrés F. Barrientos:
Differentially Private Regression Diagnostics. ICDM 2016: 81-90 - [c34]Benjamin Stoddard, Kate O'Hanlon, Brian Lin, Ashwin Machanavajjhala, Landon P. Cox:
Ayumu: Efficient lifelogging with focused tasks. MobiCASE 2016: 127-137 - [c33]Nisarg Raval, Animesh Srivastava, Ali Razeen, Kiron Lebeck, Ashwin Machanavajjhala, Landon P. Cox:
Demo: What You Mark is What Apps See. MobiSys (Companion Volume) 2016: 116 - [c32]Nisarg Raval, Animesh Srivastava, Ali Razeen, Kiron Lebeck, Ashwin Machanavajjhala, Landon P. Cox:
What You Mark is What Apps See. MobiSys 2016: 249-261 - [c31]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang:
Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154 - [c30]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang, George Bissias:
Exploring Privacy-Accuracy Tradeoffs using DPComp. SIGMOD Conference 2016: 2101-2104 - 2015
- [j19]Ashwin Machanavajjhala, Daniel Kifer:
Designing statistical privacy for your data. Commun. ACM 58(3): 58-67 (2015) - [j18]Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M. Procopiuc, Divesh Srivastava:
DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems. Proc. VLDB Endow. 8(11): 1154-1165 (2015) - [j17]Samuel Haney, Ashwin Machanavajjhala, Bolin Ding:
Design of Policy-Aware Differentially Private Algorithms. Proc. VLDB Endow. 9(4): 264-275 (2015) - [i14]Yan Chen, Ashwin Machanavajjhala:
On the Privacy Properties of Variants on the Sparse Vector Technique. CoRR abs/1508.07306 (2015) - [i13]Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang:
Principled Evaluation of Differentially Private Algorithms using DPBench. CoRR abs/1512.04817 (2015) - 2014
- [j16]Hye-Chung Kum, Ashok Kumar Krishnamurthy, Ashwin Machanavajjhala, Stanley C. Ahalt:
Social Genome: Putting Big Data to Work for Population Informatics. Computer 47(1): 56-63 (2014) - [j15]Hye-Chung Kum, Ashok K. Krishnamurthy, Ashwin Machanavajjhala, Michael K. Reiter, Stanley C. Ahalt:
Privacy preserving interactive record linkage (PPIRL). J. Am. Medical Informatics Assoc. 21(2): 212-220 (2014) - [j14]Daniel Kifer, Ashwin Machanavajjhala:
Pufferfish: A framework for mathematical privacy definitions. ACM Trans. Database Syst. 39(1): 3:1-3:36 (2014) - [c29]Nisarg Raval, Landon P. Cox, Animesh Srivastava, Ashwin Machanavajjhala, Kiron Lebeck:
MarkIt: privacy markers for protecting visual secrets. UbiComp Adjunct 2014: 1289-1295 - [c28]Xi He, Ashwin Machanavajjhala, Bolin Ding:
Blowfish privacy: tuning privacy-utility trade-offs using policies. SIGMOD Conference 2014: 1447-1458 - [i12]Samuel Haney, Ashwin Machanavajjhala, Bolin Ding:
Answering Query Workloads with Optimal Error under Blowfish Privacy. CoRR abs/1404.3722 (2014) - [i11]Ben Stoddard, Yan Chen, Ashwin Machanavajjhala:
Differentially Private Algorithms for Empirical Machine Learning. CoRR abs/1411.5428 (2014) - 2013
- [j13]Kedar Bellare, Carlo Curino, Ashwin Machanavajjhala, Peter Mika, Mandar Rahurkar, Aamod Sane:
WOO: A Scalable and Multi-tenant Platform for Continuous Knowledge Base Synthesis. Proc. VLDB Endow. 6(11): 1114-1125 (2013) - [j12]Theodoros Rekatsinas, Amol Deshpande, Ashwin Machanavajjhala:
A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries. Proc. VLDB Endow. 6(13): 1594-1605 (2013) - [c27]Jianjun Chen, Ashwin Machanavajjhala, George Varghese:
Scalable Social Coordination with Group Constraints using Enmeshed Queries. CIDR 2013 - [c26]Eunsu Ryu, Yao Rong, Jie Li, Ashwin Machanavajjhala:
curso: protect yourself from curse of attribute inference: a social network privacy-analyzer. DBSocial 2013: 13-18 - [c25]Vibhor Rastogi, Ashwin Machanavajjhala, Laukik Chitnis, Anish Das Sarma:
Finding connected components in map-reduce in logarithmic rounds. ICDE 2013: 50-61 - [c24]Lise Getoor, Ashwin Machanavajjhala:
Entity resolution for big data. KDD 2013: 1527 - [c23]Lise Getoor, Ashwin Machanavajjhala:
Network sampling. KDD 2013: 1528 - [e1]Kristen LeFevre, Ashwin Machanavajjhala, Adam Silberstein:
Proceedings of the 3rd ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013, New York, NY, USA, June, 23, 2013. ACM 2013, ISBN 978-1-4503-2191-4 [contents] - [i10]