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ORCIDSomesh Jha
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- affiliation: University of Wisconsin-Madison, Madison, USA
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2020 – today
- 2021
[j32]Varun Chandrasekaran, Chuhan Gao, Brian Tang, Kassem Fawaz, Somesh Jha, Suman Banerjee:
Face-Off: Adversarial Face Obfuscation. Proc. Priv. Enhancing Technol. 2021(2): 369-390 (2021)- [j31]Tianhao Wang
, Ninghui Li, Somesh Jha:
Locally Differentially Private Heavy Hitter Identification. IEEE Trans. Dependable Secur. Comput. 18(2): 982-993 (2021) - [c155]Washington Garcia, Animesh Chhotaray, Joseph I. Choi, Suman Kalyan Adari, Kevin R. B. Butler, Somesh Jha:
Brittle Features of Device Authentication. CODASPY 2021: 53-64 - [i57]Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang:
CaPC Learning: Confidential and Private Collaborative Learning. CoRR abs/2102.05188 (2021) - [i56]Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha:
Exploring Adversarial Robustness of Deep Metric Learning. CoRR abs/2102.07265 (2021) - [i55]Washington Garcia, Pin-Yu Chen, Somesh Jha, Scott Clouse, Kevin R. B. Butler:
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples. CoRR abs/2103.03325 (2021) - 2020
- [j30]Sanjit A. Seshia, Somesh Jha, Tommaso Dreossi:
Semantic Adversarial Deep Learning. IEEE Des. Test 37(2): 8-18 (2020) - [j29]Samuel Yeom, Irene Giacomelli, Alan Menaged, Matt Fredrikson, Somesh Jha:
Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning. J. Comput. Secur. 28(1): 35-70 (2020) - [j28]Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li, Somesh Jha:
Improving Utility and Security of the Shuffler-based Differential Privacy. Proc. VLDB Endow. 13(13): 3545-3558 (2020) - [c154]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarially Robust Learning Could Leverage Computational Hardness. ALT 2020: 364-385 - [c153]Uyeong Jang, Susmit Jha, Somesh Jha:
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses. ICLR 2020 - [c152]Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha:
Concise Explanations of Neural Networks using Adversarial Training. ICML 2020: 1383-1391 - [c151]Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha:
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. ICML 2020: 1939-1951 - [c150]Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page:
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. ICML 2020: 11235-11245 - [c149]Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha:
Crypt?: Crypto-Assisted Differential Privacy on Untrusted Servers. SIGMOD Conference 2020: 603-619 - [c148]Aiping Xiong, Tianhao Wang, Ninghui Li, Somesh Jha:
Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension. IEEE Symposium on Security and Privacy 2020: 392-410 - [c147]Zhichuang Sun, Bo Feng, Long Lu, Somesh Jha:
OAT: Attesting Operation Integrity of Embedded Devices. IEEE Symposium on Security and Privacy 2020: 1433-1449 - [c146]Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan:
Exploring Connections Between Active Learning and Model Extraction. USENIX Security Symposium 2020: 1309-1326 - [i54]Goutham Ramakrishnan, Jordan Henkel, Zi Wang, Aws Albarghouthi, Somesh Jha, Thomas W. Reps:
Semantic Robustness of Models of Source Code. CoRR abs/2002.03043 (2020) - [i53]Ryan Feng, Jiefeng Chen, Nelson R. Manohar, Earlence Fernandes, Somesh Jha, Atul Prakash:
Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification. CoRR abs/2002.07088 (2020) - [i52]Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page:
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. CoRR abs/2002.07906 (2020) - [i51]Yue Gao, Harrison Rosenberg, Kassem Fawaz, Somesh Jha, Justin Hsu:
Analyzing Accuracy Loss in Randomized Smoothing Defenses. CoRR abs/2003.01595 (2020) - [i50]Chuhan Gao, Varun Chandrasekaran, Kassem Fawaz, Somesh Jha:
Face-Off: Adversarial Face Obfuscation. CoRR abs/2003.08861 (2020) - [i49]Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
Robust Out-of-distribution Detection in Neural Networks. CoRR abs/2003.09711 (2020) - [i48]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta:
Obliviousness Makes Poisoning Adversaries Weaker. CoRR abs/2003.12020 (2020) - [i47]Aiping Xiong, Tianhao Wang, Ninghui Li, Somesh Jha:
Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension. CoRR abs/2003.13922 (2020) - [i46]Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani:
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation. CoRR abs/2004.10390 (2020) - [i45]Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, Somesh Jha:
Continuous Release of Data Streams under both Centralized and Local Differential Privacy. CoRR abs/2005.11753 (2020) - [i44]Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
Robust Out-of-distribution Detection via Informative Outlier Mining. CoRR abs/2006.15207 (2020) - [i43]Yizhen Wang, Xiaozhu Meng, Mihai Christodorescu, Somesh Jha:
Robust Learning against Logical Adversaries. CoRR abs/2007.00772 (2020) - [i42]Zi Wang, Aws Albarghouthi, Somesh Jha:
Abstract Universal Approximation for Neural Networks. CoRR abs/2007.06093 (2020) - [i41]Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee:
Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers. CoRR abs/2007.15147 (2020) - [i40]Amrita Roy Chowdhury, Bolin Ding, Somesh Jha, Weiran Liu, Jingren Zhou:
Intertwining Order Preserving Encryption and Differential Privacy. CoRR abs/2009.05679 (2020) - [i39]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr:
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR abs/2011.05315 (2020) - [i38]Zhichuang Sun, Ruimin Sun, Long Lu, Somesh Jha:
ShadowNet: A Secure and Efficient System for On-device Model Inference. CoRR abs/2011.05905 (2020) - [i37]Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri:
Sample Complexity of Adversarially Robust Linear Classification on Separated Data. CoRR abs/2012.10794 (2020)
2010 – 2019
- 2019
- [c145]Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha:
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks. EuroS&P 2019: 480-495 - [c144]Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami:
Attribution-Based Confidence Metric For Deep Neural Networks. NeurIPS 2019: 11826-11837 - [c143]Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha:
Robust Attribution Regularization. NeurIPS 2019: 14300-14310 - [c142]Tianhao Wang, Bolin Ding, Jingren Zhou, Cheng Hong, Zhicong Huang, Ninghui Li, Somesh Jha:
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy. SIGMOD Conference 2019: 159-176 - [i36]Amrita Roy Chowdhury, Chenghong Wang, Xi He, Ashwin Machanavajjhala, Somesh Jha:
Outis: Crypto-Assisted Differential Privacy on Untrusted Servers. CoRR abs/1902.07756 (2019) - [i35]Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami:
Attribution-driven Causal Analysis for Detection of Adversarial Examples. CoRR abs/1903.05821 (2019) - [i34]Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha:
Robust Attribution Regularization. CoRR abs/1905.09957 (2019) - [i33]Varun Chandrasekaran, Brian Tang, Varsha Pendyala, Kassem Fawaz, Somesh Jha, Xi Wu:
Enhancing ML Robustness Using Physical-World Constraints. CoRR abs/1905.10900 (2019) - [i32]Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarially Robust Learning Could Leverage Computational Hardness. CoRR abs/1905.11564 (2019) - [i31]Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha:
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. CoRR abs/1905.12813 (2019) - [i30]Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Ninghui Li, Somesh Jha:
Practical and Robust Privacy Amplification with Multi-Party Differential Privacy. CoRR abs/1908.11515 (2019) - [i29]Uyeong Jang, Susmit Jha, Somesh Jha:
On Need for Topology Awareness of Generative Models. CoRR abs/1909.03334 (2019) - [i28]Lakshya Jain, Wilson Wu, Steven Chen, Uyeong Jang, Varun Chandrasekaran, Sanjit A. Seshia, Somesh Jha:
Generating Semantic Adversarial Examples with Differentiable Rendering. CoRR abs/1910.00727 (2019) - 2018
- [j27]Irfan Ul Haq, Sergio Chica, Juan Caballero, Somesh Jha:
Malware lineage in the wild. Comput. Secur. 78: 347-363 (2018) - [c141]Irene Giacomelli, Somesh Jha, Marc Joye, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption. ACNS 2018: 243-261 - [c140]Tommaso Dreossi, Somesh Jha, Sanjit A. Seshia:
Semantic Adversarial Deep Learning. CAV (1) 2018: 3-26 - [c139]Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha:
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. CSF 2018: 268-282 - [c138]Yizhen Wang, Somesh Jha, Kamalika Chaudhuri:
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. ICML 2018: 5120-5129 - [c137]Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha:
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training. ICML 2018: 5330-5338 - [c136]Andrew Miller, Zhicheng Cai, Somesh Jha:
Smart Contracts and Opportunities for Formal Methods. ISoLA (4) 2018: 280-299 - [c135]Susmit Jha, Uyeong Jang, Somesh Jha, Brian Jalaian:
Detecting Adversarial Examples Using Data Manifolds. MILCOM 2018: 547-552 - [c134]Yonghwi Kwon, Fei Wang, Weihang Wang, Kyu Hyung Lee, Wen-Chuan Lee, Shiqing Ma, Xiangyu Zhang, Dongyan Xu, Somesh Jha, Gabriela F. Ciocarlie, Ashish Gehani, Vinod Yegneswaran:
MCI : Modeling-based Causality Inference in Audit Logging for Attack Investigation. NDSS 2018 - [c133]Graham Cormode, Somesh Jha, Tejas Kulkarni, Ninghui Li, Divesh Srivastava, Tianhao Wang:
Privacy at Scale: Local Differential Privacy in Practice. SIGMOD Conference 2018: 1655-1658 - [c132]Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi, Somesh Jha, Damien Octeau:
Neural-augmented static analysis of Android communication. ESEC/SIGSOFT FSE 2018: 342-353 - [c131]Tianhao Wang, Ninghui Li, Somesh Jha:
Locally Differentially Private Frequent Itemset Mining. IEEE Symposium on Security and Privacy 2018: 127-143 - [c130]Shiqing Ma, Juan Zhai, Yonghwi Kwon, Kyu Hyung Lee, Xiangyu Zhang, Gabriela F. Ciocarlie, Ashish Gehani, Vinod Yegneswaran, Dongyan Xu, Somesh Jha:
Kernel-Supported Cost-Effective Audit Logging for Causality Tracking. USENIX Annual Technical Conference 2018: 241-254 - [i27]Zhichuang Sun, Bo Feng, Long Lu, Somesh Jha:
OEI: Operation Execution Integrity for Embedded Devices. CoRR abs/1802.03462 (2018) - [i26]Tommaso Dreossi, Somesh Jha, Sanjit A. Seshia:
Semantic Adversarial Deep Learning. CoRR abs/1804.07045 (2018) - [i25]Jiefeng Chen, Xi Wu, Yingyu Liang, Somesh Jha:
Improving Adversarial Robustness by Data-Specific Discretization. CoRR abs/1805.07816 (2018) - [i24]Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi, Somesh Jha, Damien Octeau:
Neural-Augmented Static Analysis of Android Communication. CoRR abs/1809.04059 (2018) - [i23]Xiaozhu Meng, Barton P. Miller, Somesh Jha:
Adversarial Binaries for Authorship Identification. CoRR abs/1809.08316 (2018) - [i22]Washington Garcia, Joseph I. Choi, Suman Kalyan Adari, Somesh Jha, Kevin R. B. Butler:
Explainable Black-Box Attacks Against Model-based Authentication. CoRR abs/1810.00024 (2018) - [i21]Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu:
Concise Explanations of Neural Networks using Adversarial Training. CoRR abs/1810.06583 (2018) - [i20]Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan:
Model Extraction and Active Learning. CoRR abs/1811.02054 (2018) - [i19]Irene Giacomelli, Somesh Jha, Ross Kleiman, David Page, Kyonghwan Yoon:
Privacy-Preserving Collaborative Prediction using Random Forests. CoRR abs/1811.08695 (2018) - 2017
- [j26]William R. Harris, Somesh Jha, Thomas W. Reps, Sanjit A. Seshia:
Program synthesis for interactive-security systems. Formal Methods Syst. Des. 51(2): 362-394 (2017) - [c129]Uyeong Jang, Xi Wu, Somesh Jha:
Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning. ACSAC 2017: 262-277 - [c128]Vaibhav Rastogi, Chaitra Niddodi, Sibin Mohan, Somesh Jha:
New Directions for Container Debloating. FEAST@CCS 2017: 51-56 - [c127]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami:
Practical Black-Box Attacks against Machine Learning. AsiaCCS 2017: 506-519 - [c126]Drew Davidson, Yaohui Chen, Franklin George, Long Lu, Somesh Jha:
Secure Integration of Web Content and Applications on Commodity Mobile Operating Systems. AsiaCCS 2017: 652-665 - [c125]Lorenzo De Carli, Ruben Torres, Gaspar Modelo-Howard, Alok Tongaonkar, Somesh Jha:
Botnet protocol inference in the presence of encrypted traffic. INFOCOM 2017: 1-9 - [c124]Lorenzo De Carli, Ruben Torres, Gaspar Modelo-Howard, Alok Tongaonkar, Somesh Jha:
Kali: Scalable encryption fingerprinting in dynamic malware traces. MALWARE 2017: 3-10 - [c123]Drew Davidson, Vaibhav Rastogi, Mihai Christodorescu, Somesh Jha:
Enhancing Android Security Through App Splitting. SecureComm 2017: 24-44 - [c122]Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics. SIGMOD Conference 2017: 1307-1322 - [c121]Vaibhav Rastogi, Drew Davidson, Lorenzo De Carli, Somesh Jha, Patrick D. McDaniel:
Cimplifier: automatically debloating containers. ESEC/SIGSOFT FSE 2017: 476-486 - [c120]Tianhao Wang, Jeremiah Blocki, Ninghui Li, Somesh Jha:
Locally Differentially Private Protocols for Frequency Estimation. USENIX Security Symposium 2017: 729-745 - [i18]Rathijit Sen, Jianqiao Zhu, Jignesh M. Patel, Somesh Jha:
ROSA: R Optimizations with Static Analysis. CoRR abs/1704.02996 (2017) - [i17]Tianhao Wang, Jeremiah Blocki, Ninghui Li, Somesh Jha:
Optimizing Locally Differentially Private Protocols. CoRR abs/1705.04421 (2017) - [i16]Yizhen Wang, Somesh Jha, Kamalika Chaudhuri:
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. CoRR abs/1706.03922 (2017) - [i15]Adwait Nadkarni, William Enck, Somesh Jha, Jessica Staddon:
Policy by Example: An Approach for Security Policy Specification. CoRR abs/1707.03967 (2017) - [i14]Tianhao Wang, Ninghui Li, Somesh Jha:
Locally Differentially Private Heavy Hitter Identification. CoRR abs/1708.06674 (2017) - [i13]Samuel Yeom, Matt Fredrikson, Somesh Jha:
The Unintended Consequences of Overfitting: Training Data Inference Attacks. CoRR abs/1709.01604 (2017) - [i12]Irfan Ul Haq, Sergio Chica, Juan Caballero, Somesh Jha:
Malware Lineage in the Wild. CoRR abs/1710.05202 (2017) - [i11]Xi Wu, Uyeong Jang, Lingjiao Chen, Somesh Jha:
Manifold Assumption and Defenses Against Adversarial Perturbations. CoRR abs/1711.08001 (2017) - [i10]Irene Giacomelli, Somesh Jha, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression on Distributed Data. IACR Cryptol. ePrint Arch. 2017: 707 (2017) - [i9]Irene Giacomelli, Somesh Jha, Marc Joye, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption. IACR Cryptol. ePrint Arch. 2017: 979 (2017) - 2016
- [j25]Damien Octeau, Daniel Luchaup, Somesh Jha, Patrick D. McDaniel:
Composite Constant Propagation and its Application to Android Program Analysis. IEEE Trans. Software Eng. 42(11): 999-1014 (2016) - [c119]Xi Wu, Matthew Fredrikson, Somesh Jha, Jeffrey F. Naughton:
A Methodology for Formalizing Model-Inversion Attacks. CSF 2016: 355-370 - [c118]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. EuroS&P 2016: 372-387 - [c117]Damien Octeau, Somesh Jha, Matthew Dering, Patrick D. McDaniel, Alexandre Bartel, Li Li, Jacques Klein, Yves Le Traon:
Combining static analysis with probabilistic models to enable market-scale Android inter-component analysis. POPL 2016: 469-484 - [c116]Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami:
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. IEEE Symposium on Security and Privacy 2016: 582-597 - [c115]Adwait Nadkarni, Benjamin Andow, William Enck, Somesh Jha:
Practical DIFC Enforcement on Android. USENIX Security Symposium 2016: 1119-1136 - [i8]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami:
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. CoRR abs/1602.02697 (2016) - [i7]Vaibhav Rastogi, Drew Davidson, Lorenzo De Carli, Somesh Jha, Patrick D. McDaniel:
Towards Least Privilege Containers with Cimplifier. CoRR abs/1602.08410 (2016) - [i6]Xi Wu, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton:
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics. CoRR abs/1606.04722 (2016) - 2015
- [c114]Matt Fredrikson, Somesh Jha, Thomas Ristenpart:
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. CCS 2015: 1322-1333 - [c113]Damien Octeau, Daniel Luchaup, Matthew Dering, Somesh Jha, Patrick D. McDaniel:
Composite Constant Propagation: Application to Android Inter-Component Communication Analysis. ICSE (1) 2015: 77-88 - [i5]Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami:
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. CoRR abs/1511.04508 (2015) - [i4]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. CoRR abs/1511.07528 (2015) - [i3]Xi Wu, Matthew Fredrikson, Wentao Wu, Somesh Jha, Jeffrey F. Naughton:
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences. CoRR abs/1512.06388 (2015) - 2014
- [c112]Daniel Luchaup, Thomas Shrimpton, Thomas Ristenpart, Somesh Jha:
Formatted Encryption Beyond Regular Languages. CCS 2014: 1292-1303 - [c111]Lorenzo De Carli, Robin Sommer, Somesh Jha:
Beyond Pattern Matching: A Concurrency Model for Stateful Deep Packet Inspection. CCS 2014: 1378-1390 - [c110]Matthew Fredrikson, Somesh Jha:
Satisfiability modulo counting: a new approach for analyzing privacy properties. CSL-LICS 2014: 42:1-42:10 - [c109]Daniel Luchaup, Lorenzo De Carli, Somesh Jha, Eric Bach:
Deep packet inspection with DFA-trees and parametrized language overapproximation. INFOCOM 2014: 531-539 - [c108]Richard Joiner, Thomas W. Reps, Somesh Jha, Mohan Dhawan, Vinod Ganapathy:
Efficient runtime-enforcement techniques for policy weaving. SIGSOFT FSE 2014: 224-234 - [c107]Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon M. Lin, David Page, Thomas Ristenpart:
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. USENIX Security Symposium 2014: 17-32 - [c106]Daniel Luchaup, Kevin P. Dyer, Somesh Jha, Thomas Ristenpart, Thomas Shrimpton:
LibFTE: A Toolkit for Constructing Practical, Format-Abiding Encryption Schemes. USENIX Security Symposium 2014: 877-891 - 2013
- [j24]Martin Franz, Björn Deiseroth, Kay Hamacher, Somesh Jha, Stefan Katzenbeisser, Heike Schröder:
Secure computations on non-integer values with applications to privacy-preserving sequence analysis. Inf. Secur. Tech. Rep. 17(3): 117-128 (2013) - [c105]William R. Harris, Guoliang Jin, Shan Lu, Somesh Jha:
Validating Library Usage Interactively. CAV 2013: 796-812 - [c104]Florian Sagstetter, Martin Lukasiewycz, Sebastian Steinhorst, Marko Wolf, Alexandre Bouard, William R. Harris, Somesh Jha, Thomas Peyrin, Axel Poschmann, Samarjit Chakraborty:
Security challenges in automotive hardware/software architecture design. DATE 2013: 458-463 - [c103]Somesh Jha, Thomas W. Reps, William R. Harris:
Secure programs via game-based synthesis. FMCAD 2013: 12-13 - [c102]Somesh Jha, Matthew Fredrikson, Mihai Christodorescu, Reiner Sailer, Xifeng Yan:
Synthesizing near-optimal malware specifications from suspicious behaviors. MALWARE 2013: 41-50 - [c101]William R. Harris, Somesh Jha, Thomas W. Reps, Jonathan Anderson, Robert N. M. Watson:
Declarative, Temporal, and Practical Programming with Capabilities. IEEE Symposium on Security and Privacy 2013: 18-32 - [c100]Drew Davidson, Benjamin Moench, Thomas Ristenpart, Somesh Jha:
FIE on Firmware: Finding Vulnerabilities in Embedded Systems Using Symbolic Execution. USENIX Security Symposium 2013: 463-478 - [c99]Damien Octeau, Patrick D. McDaniel, Somesh Jha, Alexandre Bartel, Eric Bodden, Jacques Klein, Yves Le Traon:
Effective Inter-Component Communication Mapping in Android: An Essential Step Towards Holistic Security Analysis. USENIX Security Symposium 2013: 543-558 - 2012
- [j23]Paul F. Syverson, Somesh Jha:
Guest Editorial: Special Issue on Computer and Communications Security. ACM Trans. Inf. Syst. Secur. 15(1): 1:1-1:2 (2012) - [c98]Matthew Fredrikson, Richard Joiner, Somesh Jha, Thomas W. Reps, Phillip A. Porras, Hassen Saïdi, Vinod Yegneswaran:
Efficient Runtime Policy Enforcement Using Counterexample-Guided Abstraction Refinement. CAV 2012: 548-563 - [c97]William R. Harris, Somesh Jha, Thomas W. Reps:
Secure Programming via Visibly Pushdown Safety Games. CAV 2012: 581-598 - [c96]