{"id":223,"date":"2026-06-13T22:10:43","date_gmt":"2026-06-13T22:10:43","guid":{"rendered":"https:\/\/www.isi.edu\/centers-pdasp\/?page_id=223"},"modified":"2026-09-24T23:54:22","modified_gmt":"2026-09-24T23:54:22","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.isi.edu\/centers-pdasp\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n\n\t<h3><b>TRACK 1: Advancing key technologies to enable practical PPDSA solutions<\/b><\/h3>\n<b>PDaSP Track 1: Enabling a Privacy-Preserving Data Life Cycle with Lightweight Secure Computation<\/b>, Henry Corrigan-Gibbs (PI), UC Berkeley and Emma Dauterman (Co-PI), Stanford\n<ul>\n  <li>Alexandra Henzinger, Emma Dauterman, Henry Corrigan-Gibbs, and Dan Boneh. &#8220;Nudge: A Private Recommendations Engine.&#8221; USENIX Security 2026.<\/li>\n  <li>Ryan Lehmkuhl, Henry Corrigan-Gibbs, Emma Dauterman, and David J. Wu. &#8220;Heli: Heavy-Light Private Aggregation.&#8221; USENIX Security 2026.<\/li>\n<\/ul>\n<b>PDaSP Track 1: Practical Secure Multiparty Computations for Graph-based Intrusion Detection Systems<\/b>, Yupeng Zhang (PI), University of Illinois at Urbana-Champaign and Li Zhou (Co-PI), University of California-Irvine\n<ul>\n  <li>Yu Zheng, Chenang Li, Zhou Li and Qingsong Wang. Convergent Privacy Framework for Multi-layer GNNs through Contractive Message Passing. Network and Distributed System Security Symposium (NDSS), February, 2026. doi: <a href=\"https:\/\/dx.doi.org\/10.14722\/ndss.2026.240255\">10.14722\/ndss.2026.240255<\/a><\/li>\n<\/ul>\n<hr \/>\n<h3><b>TRACK 2: Integrated and comprehensive solutions for trustworthy data sharing in application settings<\/b><\/h3>\n<b>PDaSP Track 2: A Holistic Privacy Preserving Collaborative Data Sharing System for Intelligent Transportation<\/b>, Xuegang Ban (PI) and Angela Kitali (Co-PI), University of Washington, Yuan Hong (PI) and Song Han (Co-PI), University of Connecticut, Binghui Wang (PI), Illinois Institute of Technology and Meisam Mohammady (PI), Iowa State University \n<ul>\n    <li>Qin Yang, Nicholas Stout, Meisam Mohammady, Han Wang, Ayesha Samreen, Christopher Quinn, Yan Yan, Ashish Kundu, and Yuan Hong. &#8220;PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization.&#8221; CCS 2025. doi: <a href=\"https:\/\/doi.org\/10.1145\/3719027.3765151\">https:\/\/doi.org\/10.1145\/3719027.3765151<\/a><\/li>\n    <li>Shuya Feng and Yuan Hong. &#8220;DPED: Multi-Layer Noise Distillation for Privacy-Preserving Text Embeddings.&#8221; EMNLP 2025. doi: <a href=\"https:\/\/doi.org\/10.18653\/v1\/2025.emnlp-main.1282\">https:\/\/doi.org\/10.18653\/v1\/2025.emnlp-main.1282<\/a><\/li>\n    <li>Joshua Lee, Ali Arastehfard, Weiran Liu, Xuegang Ban, and Yuan Hong. &#8220;SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications.&#8221; SAC 2026. doi: <a href=\"https:\/\/doi.org\/10.1145\/3748522.3779738\">https:\/\/doi.org\/10.1145\/3748522.3779738<\/a><\/li>\n<\/ul>\n<b>PDaSP Track 2: Confidential Genome Imputation and Analytics (CoGIA)<\/b>, Hyunghoon Cho (PI), Suleyman Sahinalp (Co-PI) and Fan Zhang (Co-PI), Yale University\n<b>PDaSP Track 2: Explainable Auditing of ML Models for Privacy Violations<\/b>, Bradley Malin (PI), Vanderbilt University Medical Center, Netanel Raviv (PI) and Yevgeniy Vorobeychik (Co-PI), Washington University, and Murat Kantarcioglu (PI), Virginia Polytechnic Institute and State University\n<b>PDASP Track 2: TIDES &#8211; Building a Trusted Integration Data Exchange System<\/b>, Sebastian Angel (PI), Andreas Haeberlen (Co-PI), Brett Falk (Co-PI), Ryan Marcus (Co-PI) and Pratyush Mishra (Co-PI), University of Pennsylvania\n<b>PDaSP Track 2: Secure Analytics over Federated Environments for Biomedical Science (SAFE-Bio)<\/b>, Ronald Hutchins (PI), University of Virginia, Jelena Mirkovic (Co-PI), USC Information Sciences Institute, Paul England (Co-PI) and John Manferdelli (Co-PI), Datica Research\n<hr \/>\n<h3><b>TRACK 3: Usable tools, and testbeds for trustworthy sharing of private or otherwise confidential data<\/b><\/h3>\n<b>PDaSP Track 3: Privacy-Preserving Dairy-Digitalization with Federated Learning<\/b>, Miel Hostens (PI), Cornell University and Joao Dorea (Co-PI), UW-Madison\n<ul>\n  <li>Hostens, Miel, S\u00e9bastien Franceschini, Meike van Leerdam, et al. 2025. &#8220;The Future of Big Data and Artificial Intelligence on Dairy Farms: A Proposed Dairy Data Ecosystem.&#8221; JDS Communications 6: S9-14. <a href=\"https:\/\/doi.org\/https:\/\/doi.org\/10.3168\/jdsc.2025-0843\">https:\/\/doi.org\/https:\/\/doi.org\/10.3168\/jdsc.2025-0843<\/a><\/li>\n  <li>Liu, Enhong, Haiyu Yang, and Miel Hostens. 2025. Evaluating Small Language Models for Agentic on-Farm Decision Support Systems. <a href=\"https:\/\/arxiv.org\/abs\/2512.14043\">https:\/\/arxiv.org\/abs\/2512.14043<\/a><\/li>\n  <li>Liu, E., H. Yang, S. Sharma, et al. 2025. &#8220;Agents Are All You Need: Pioneering the Use of Agentic Artificial Intelligence to Embrace Large Language Models into Dairy Science.&#8221; Journal of Dairy Science 108 (12): 14038-49. <a href=\"https:\/\/doi.org\/https:\/\/doi.org\/10.3168\/jds.2025-26775\">https:\/\/doi.org\/https:\/\/doi.org\/10.3168\/jds.2025-26775<\/a><\/li>\n  <li>Sharma, S., Liu, E., van Leerdam, M., Hu, H., Villalobos-Barquero, R., Dorea, J. R., &#8230; &amp; Hostens, M. (2026). Invited review: Milking the data for value-driven dairy farming. Journal of Dairy Science<\/li>\n<\/ul>\n<b>PDaSP Track 3: Testbed for Enhancing Privacy and Robustness of Federated Learning Systems<\/b>, Fatima Anwar (PI), University of Massachusetts Amherst, Muhammad Ali Gulzar (PI), Virginia Polytechnic Institute and State University, and Ali Anwar (PI), University of Minnesota-Twin Cities\n<b>PDaSP: Track 3: Rigorous and Performant Differentially Private Machine Learning via OpenDP<\/b>, Salil Vadhan (PI) and Flavio Calmon (Co-PI), Harvard University\n<b>PDaSP: Track 3: TEPPIT: TEstbed for Privacy-PreservIng Technologies for Data Sharing and Analysis<\/b>, Jelena Mirkovic (PI), John Heidemann (Co-PI) and Jose-Luis Ambite (Co-PI), University of Southern California\n\n","protected":false},"excerpt":{"rendered":"<p>TRACK 1: Advancing key technologies to enable practical PPDSA solutions PDaSP Track 1: Enabling a Privacy-Preserving Data Life Cycle with Lightweight Secure Computation, Henry Corrigan-Gibbs (PI), UC Berkeley and Emma Dauterman (Co-PI), Stanford Alexandra Henzinger, Emma Dauterman, Henry Corrigan-Gibbs, and Dan Boneh. &#8220;Nudge: A Private Recommendations Engine.&#8221; USENIX Security 2026. Ryan Lehmkuhl, Henry Corrigan-Gibbs, Emma&hellip;<\/p>\n","protected":false},"author":699,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"news_source":"","news_author":"","external_news_link":"","footnotes":""},"class_list":["post-223","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Publications - Privacy-Preserving Data Sharing in Practice (PDaSP)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.isi.edu\/centers-pdasp\/publications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Publications - Privacy-Preserving Data Sharing in Practice (PDaSP)\" \/>\n<meta property=\"og:description\" content=\"TRACK 1: Advancing key technologies to enable practical PPDSA solutions PDaSP Track 1: Enabling a Privacy-Preserving Data Life Cycle with Lightweight Secure Computation, Henry Corrigan-Gibbs (PI), UC Berkeley and Emma Dauterman (Co-PI), Stanford Alexandra Henzinger, Emma Dauterman, Henry Corrigan-Gibbs, and Dan Boneh. &#8220;Nudge: A Private Recommendations Engine.&#8221; USENIX Security 2026. 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