Yuechun Gu, Jiajie He, and Keke Chen, "Auditing Approximate Machine Unlearning for Differentially Private Models", arXiv, 2025

Jiajie He, Yuechun Gu, Min-Chun Chen, and Keke Chen, "Membership Inference Attacks on LLM-based Recommender Systems", arXiv, 2025

Jiajie He, Yuechun Gu, and Keke Chen, "RecPS: Privacy Risk Scoring for Recommender Systems", in Proceedings of ACM Recommender Systems (RecSys), Prague, 2025

Yuechun Gu, Jiajie He, and Keke Chen, "Adaptive Domain Inference Attack with Concept Hierarchy", in Proceedings of ACM SIGKDD conference, Toronto, 2025

Yuechun Gu and Keke Chen, "Calibrating Practical Privacy Risks for Differentially Private Machine Learning," in Proceedings of IEEE BigData, Washington DC, 2024

Mubashwir Alam and Keke Chen, "TEE-MR: Developer-friendly data oblivious programming for trusted execution environments," in Computers and Security Journal, Vol 148, 2024

Yuechun Gu, Jiajie He, and Keke Chen, "Demo: FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation," ACM CCS Demo session, Salt Lake City, Utah 2024

Mubashwir Alam and Keke Chen, "TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis", in Frontiers in Big Data, 2023 

Allison Scarbrough, Keke Chen, Bing Yu, "Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra", to appear in the Journal of Biomedical Optics, 2023

Yuechun Gu, Sagar Sharma, and Keke Chen, "Demo: Image Disguising for Scalable GPU-accelerated Confidential Deep Learning," ACM CCS, demo session, Denmark, 2023

Keke Chen, Yuechun Gu, and Sagar Sharma, "DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds", ACM Transactions on Internet Technology, volume 23, issue 3, 2023

Mubashwir Alam and Keke Chen, "Making Your Program Oblivious: a Comparative Study for Side-channel-safe Confidential Computing", IEEE Conference on Cloud Computing (CLOUD), Chicago, 2023

Mubashwir Alam, Justin Boyce, and Keke Chen, "Demo: SGX-MR-Prot: Efficient and Developer-Friendly Access-Pattern Protection in Trusted Execution Environments, IEEE Conference on Distributed Computing Systems (ICDCS), Hong Kong, China, 2023, arxiv

Yuechun Gu and Keke Chen, "GAN-Based Domain Inference Attack," AAAI 2023. arxiv

Keke Chen, "Confidential High-Performance Computing in the Public Cloud," IEEE Internet Computing, accepted in 2022, arxiv

Sagar Sharma, Yuechun Gu, and Keke Chen, "A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning," 2022, arxiv

Prem Bhusal, AKM Mubashwir Alam, Keke Chen, Ning Jiang, Yun Xing, and Jun Xiao, "Scalable Sequence Clustering for Large-Scale Immune Repertoire Analysis", IEEE BigData Conference, 2021

Sagar Sharma, Mubashwir Alam, and Keke Chen "Image Disguising for Protecting Data and Model Confidentiality in Outsourced Deep Learning", IEEE CLOUD Conference, 2021

Sagar Sharma and Keke Chen, "Confidential Machine Learning on Untrusted Platforms: A Survey", Cybersecurity Journal, Vol. 4, No. 30,  Springer, 2021,  arxiv version.

Mubashwir Alam, Sagar Sharma, and Keke Chen, "SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications", Proceedings of Privacy Enhancing Technologies (PoPETs), Sydney,  Australia, 2021, accepted in 2020

 Sagar Sharma and Keke Chen "Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data", European Symposium on Research in Computer Security (ESORICS), 2019 [pdf]

 Shreyansh Bhatt, Keke Chen, Valerie Shalin, Amit Sheth, and Brandon Minnery, Who should be the captain this week? Leveraging inferred diversity-enhanced crowd wisdom for a Fantasy Premier League prediction task, International Conference on Web and Social Media (ICWSM), 2019 [pdf]

Shreyansh Bhatt, Swati Padhee, Keke Chen, Valerie Shalin, Derek Doran, Amit Sheth and Brandon Minnery, Knowledge graph enhanced community detection and characterization, ACM Conference on Web Search and Data Mining (WSDM), 2019 [pdf]

Sagar Sharma, James Powers, and Keke Chen "PrivateGraph: Privacy-Preserving Spectral Analysis of Encrypted Graphs in the Cloud" , IEEE Transactions on Knowledge and Data Engineering, Volume 31, Issue 4, 2018. [pdf]

Sagar Sharma and Keke Chen, Image Disguising for Privacy-preserving Deep Learning, ACM CCS Poster Session, Toronto 2018 [pdf]

Sagar Sharma and Keke Chen, Privacy-Preserving Boosting with Random Linear Classifiers, ACM CCS Poster Session, Toronto 2018 [pdf]

Sagar Sharma, Keke Chen, and Amit Sheth, "Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems", IEEE Internet Computing, 2018 (online 2017) [pdf]

Keke Chen, Bharath Avusherla, Sarah Allison, Vincent Schmidt: "SPIN: Cleaning, Monitoring, and Querying Image Streams Generated by Ground-Based Telescopes for Space Situational Awareness ", Journal of Data Science and Analytics, Vol. 5, Issue 2-3, 2018 (online 2017). [pdf]

Sagar Sharma,Keke Chen: Privacy-Preserving Boosting with Random Linear Classifiers for Learning from User-Generated Data. CoRR abs/1802.08288 (2018) [pdf]

Wenbo Wang, Lu Chen, Keke Chen, Krishnaprasad Thirunarayan, and Amit Sheth: Adaptive Training Instance Selection for Cross-Domain Emotion Identification, ACM/IEEE Web Intelligence, Germany, 2017 [pdf]

Sagar Sharma and Keke Chen, PrivateGraph: A Cloud-Centric System for Privacy-Preserving Spectral Analysis of Large Encrypted Graphs, demo session of ICDCS 2017, Atlanta, GA [pdf]

Ben S. Wendel, Chenfeng He, Mingjuan Qu, Di Wu, Stefany M. Hernandez, Ke-Yue Ma, Eugene W. Liu, Jun Xiao, Peter D. Crompton, Susan K. Pierce, Pengyu Ren, Keke Chen, and Ning Jiang: " Accurate immune repertoire sequencing reveals malaria infection driven antibody lineage diversification in young children ", Nature Communications, 2017. [pdf]

Keke Chen, Venkata Gogu, Di Wu, and Ning Jiang, "COLT: Constrained Lineage Tree Generation with Sequence Data", IEEE Conference on Biomedical Informatics and Biomedicine, Shenzhen, China, 2016[pdf]

Shen Yan, Shiran Pan, Wen-Tao Zhu, and Keke Chen, "DynaEgo: Privacy-Preserving Collaborative Filtering Recommender System Based on Social-Aware Differential Privacy", International Conference on Information and Communications Security (ICICS), Singapore, 2016[pdf]

Sagar Sharma, James Powers, and Keke Chen, "Privacy-Preserving Spectral Analysis of Large Graphs in Public Clouds", ACM Symposium on InformAtion, Computer and Communications Security (ASIACCS), Xi'an, China, 2016[pdf]

Keke Chen, Bin Wang, and Prabhaker Mateti, "CUTE Labs: Low-Cost Open-Source Instructional Laboratories for Cloud Computing Education", American Society for Engineering Education Annual Conference, New Orleans, 2016[pdf]

Lu Zhou, Wenbo Wang, and Keke Chen, "Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones", International World Wide Web (WWW) Conference, Montreal, Canada, 2016[pdf]

Keke Chen and Shumin Guo: "RASP-Boost: Confidential Boosting-Model Learning with Perturbed Data in the Cloud ", to appear in IEEE Transactions on Cloud Computing, 2015. [pdf]

Zohreh Alavi, Sagar Sharma, Lu Zhou, and Keke Chen, "Scalable Euclidean Embedding for Big Data", IEEE International Conference on Cloud Computing (CLOUD), New York City, 2015 [pdf]

Lu Zhou, Wenbo Wang, and Keke Chen "Identifying Regrettable Messages from Tweets", International conference on WWW, poster session, Florence, Italy, 2015 [pdf]

Huiqi Xu, Shumin Guo, and Keke Chen: "Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation ", Volume 26, Issue 2, 2014 (Online 2012). [long version with refined proofs and corrected notations]

Keke Chen, James Powers, Shumin Guo, and Fengguang Tian: "CRESP: Towards Optimal Resource Provisioning for MapReduce Computing in Public Clouds ", IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 25, Number 6, 2014. [pdf]

Zohreh Alavi, Lu Zhou, James Powers, and Keke Chen, "RASP-QS: Efficient and Confidential Query Services in the Cloud", International Conference on Very Large Databases (VLDB), Demonstration Session, Hangzhou, China, 2014 [pdf]

Shumin Guo and Keke Chen, "PerturBoost: Practical Confidential Classifier Learning in the Cloud", IEEE Conference on Data Mining (ICDM), Dallas, Texas, 2013 [pdf]

James Powers and Keke Chen, "Secure Computation of Top-K Eigenvectors for Shared Matrices in the Cloud", IEEE Cloud Computing Conference (CLOUD), 2013. [pdf]

Shumin Guo, and Keke Chen. "Privacy Preserving Boosting in the Cloud with Secure Half-Space Queries", Poster Session of ACM Conference on Computer and Communications Security (CCS), 2012. [with corrected notations pdf]

Keke Chen: "Optimizing Star-coordinate Visualization Models for Effective Interactive Cluster Exploration on Big Data ", Journal of Intelligent Data Analysis, accepted in August, 2012, published in 2014. [pdf]

Shumin Guo, and Keke Chen. "Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services", ASE/IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT), 2012. [ pdf]

Huiqi Xu, Zhen Li, Shumin Guo, and Keke Chen. "CloudVista: Interactive and Economical Visual Cluster Analysis for Big Data in the Cloud". Demonstration Session of International Conferences on Very Large Databases, Istanbul, Turkey 2012. [ pdf]

Keke Chen, Jing Bai, and Zhaohui Zheng: "Ranking Function Adaptation with Boosting Trees ", ACM Transactions on Information Systems (TOIS), accepted in July, 2011. [pdf]

Jinpeng Wei, Calton Pu, and Keke Chen. "Flying under the Radar: Maintaining Control of Kernel without Changing Kernel Code or Persistent Data Structures". The 7th Annual Cyber Security and Information Intelligence Research Workshop (CSIIRW), October 12 - 14, 2011. [ pdf]

Keke Chen, Huiqi Xu, Fengguang Tian, Shumin Guo, " CloudVista: visual cluster exploration for extreme scale data in the cloud ", Scientific and Statistical Database Management Conference, Portland OR, 2011 [ pdf]

Fengguang Tian, Keke Chen, " Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds", IEEE Conference on Cloud Computing, Washington DC, 2011[ pdf]

Keke Chen, Ramakanth Kavuluru, Shumin Guo " RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databases ", ACM Conference on Data and Application Security and Privacy (CODASPY), 2011. [ pdf]

Keke Chen and Ling Liu, " Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining ", Journal of Knowledge and Information Systems (KAIS), 2011 (online version, 2010) [pdf]

Hua Yan, Keke Chen and Ling Liu, "SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data ", Journal of Data Mining and Knowledge Discovery (DMKD), 2010. [pdf]

Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng and Keke Chen, " Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking ", International Conference on Computational Linguistics (COLING), 2010. [ pdf]

Keke Chen and Ling Liu: " Privacy-preserving Multiparty Collaborative Mining with Geometric Data Perturbation, IEEE Transactions on Parallel and Distributed Systems, 2009 [pdf]

Keke Chen and Ling Liu: " HE-Tree: a Framework for Detecting Changes in Clustering Structure for Categorical Data Streams ", VLDB Journal, 2009 [pdf]

Keke Chen, Jing Bai, Srihari Reddy, and Belle Tseng: " On Domain Similarity and Effectiveness of Adapting to Rank " (short paper), ACM Conference on Information and Knowledge Management (CIKM09), 2009. [ pdf]

Keke Chen and Fengguang Tian: " VisGBT: Visually Analyzing Evolving Datasets for Adaptive Learning ", The 5th International Conference on Collaborative Computing (CollaborateCom09), 2009. [pdf]

Keke Chen, Rongqing Lu, CK Wong, Gordon Sun, Larry Heck, and Belle Tseng: " Trada: a Tree-Based Ranking Function Adaptation Approach", ACM Conference on Information and Knowledge Management (CIKM08), 2008. [pdf]

Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha and Gordon Sun: " Adapting Ranking Functions to User Preference ", IEEE Conference on Data Engineering, Workshop on Ranking in Databases, 2008. [pdf]

Keke Chen and Ling Liu: " Best K: the Critical Clustering Structures in Categorical Data ", Knowledge and Information Systems, 2008 [pdf]

Hua Yan, Keke Chen and Ling Liu, "Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach ", Journal of Data and Knowledge Engineering (DKE), 2008. [pdf]

Yongjing Lin, Wenyuan Li, Keke Chen and Ying Liu, " Document Clustering and Ranking System for Exploring MEDLINE Citations ", Volume 14, Issue 5,2007, Journal of the American Medical Informatics Association (JAMIA). [pdf]

Keke Chen, Gordon Sun, and LingLiu: "Towards Attack-Resilient Geometric Data Perturbation ", SIAM International Conference on Data Mining, 2007. [pdf]

Zhaohui Zheng, Keke Chen, Hongyuan Zha, Gordon Sun: "A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments ", SIGIR, 2007. [pdf]

Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun: " A General Boosting Method and its Application to Learning Ranking Functions for Web Search", in Proceedings of Neural Information Processing Systems (NIPS), Canada, 2007. [pdf]

Keke Chen and Ling liu: "Space Adaptation: Privacy-preserving Multiparty Collaborative Mining with Geometric Perturbation ", ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC 2007) (Brief Announcement) [pdf]

Keke Chen and Ling Liu: "Detecting Change of Clustering Structure in Categorical Data Stream ", SIAM Internatational Conference on Data Mining Conference(SDM), 2006. [pdf]

Hua Yan, Keke Chen and Ling Liu, " Efficiently Clustering Transactional Data with Weighted Coverage Density", ACM Conference on Information and Knowledge Management (CIKM2006) , November, 2006 [pdf]

Keke Chen and Ling Liu: " iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets " ACM Transactions on Information Systems (TOIS), Vol24, issue 2, 2006 . [pdf]

Keke Chen and Ling Liu: "Privacy-Preserving Data Classification with Rotation Perturbation ", Proc. of IEEE Intl. Conf on Data Mining 2005 (ICDM05). [pdf]

Keke Chen and Ling Liu: "The Best K" for Entropy-based Categorical Clustering ", Proc of Scientific and Statistical Database Management (SSDBM05). Santa Barbara, CA June 2005. [pdf]

Keke Chen and Ling Liu: "VISTA: Validating and Refining Clusters via Visualization." Journal of Information Visualization. Sept. 2004. [pdf]

Keke Chen and Ling Liu: "ClusterMap: Labeling Large Datasets via Visualization." ACM Conf. of Information and Knowledge Management (CIKM04), Washington DC, Nov, 2004 [pdf]

Keke Chen and Ling Liu: "Validating and Refining Clusters via Visual Rendering." Proc. of Intl. Conf. on Data Mining(ICDM03). Melbourne, FL, November 2003. [pdf]

Keke Chen and Ling Liu: "A Visual Framework Invites Human into the Clustering Process." Proc. of Scientific and Statistical Database Management (SSDBM03). Cambridge, Boston, July 2003. [pdf]

Keke Chen and Ling Liu: "Cluster Rendering of Skewed Datasets via Visualization." Proc. of ACM Symposium on Applied Computing(ACM SAC03). Melbourne, FL, March 2003. [pdf]

Keke Chen, Shumin Guo, James Powers, and Fengguang Tian: "Towards Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud." In "Data Processing Techniques in the Era of Big Data"". Edited by Sherif Sakr and Mohamed Medhat Gaber, published by CRC Press, to appear in 2013. [pdf]

Keke Chen and Ling Liu: "A Survey of Multiplicative Data Perturbation for Privacy Preserving Data Mining." In Privacy-Preserving Data Mining: Models and Algorithms". Edited by Charu C. Aggarwal and Philip S. Yu, 2008. [pdf]

Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, and Gordon Sun: Systems and Apparatus for Using B Measures to Learn Balanced Relevance Functions from Expert and User Judgments, US8126839, filing date: May 23, 2007, issue date: Mar 30, 2010

Keke Chen, Rongqing Lu, CK Wong, Gordon Sun, Larry Heck, and Belle Tseng: Methods and Apparatuses for Adapting a Ranking Function of a Search Engine for Use with a Specific Domain, US7685078, filing date: Jun 19, 2008, issue date: Feb 28 2012

Zhaohui Zheng, Hongyuan Zha, Keke Chen, and Gordon Sun: A Regression Framework for Learning Ranking Functions Using Relative Preferences, US20080208836, filing date: Feb 23, 2007, issue date: August 28, 2008