Xi He

News

• I am now a Faculty Member and a Canada CIFAR AI Chair at the Vector Institute.
• New paper accepted at VLDB'23: "Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration" (with Miti Mazmudar, Thomas Humphries, Jiaxiang Liu, Matthew Rafuse), link
• Our proposal on "Differential privacy for multi-relational databases" won the 2022 Meta Privacy Enhancing Technologies Research Award!
• New paper accepted at VLDB'22: "Don't Be a Tattle-Tale: Preventing Leakages through Data Dependencies on Access Control Protected Data" (with Primal Pappachan, Shufan Zhang, Sharad Mehrotra), (arXiv)
• New paper accepted at VLDB'22: "MIDE: Accuracy Aware Minimally Invasive Data Exploration for Decision Support" (with Sameera Ghayyur, Dhrubajyoti Ghosh, Sharad Mehrotra), (pdf)
• New paper accepted at AAAI'22: "The Role of Adaptive Optimizers for Honest Private Hyperparameter Tuning" (with Shubhankar Mohapatra, Sajin Sasy, Gautam Kamath, and Om Thakkar), (arXiv)
• New paper accepted at PETS'22: "Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases" (with Priyanka Nanayakkara, Johes Bater, Jessica Hullman, and Jennie Rogers), (arXiv) (demo)
• New book "Differential Privacy for Databases", Foundations and Trends® in Databases
• We are hiring! We are looking for grad students and postdocs interested in research in privacy and security that supports data management and machine learning. You are encouraged to read our recent work before contacting me.

Research

My research interests span the areas of privacy and security for big-data management and analysis.

• CAPE: Cost Aware Privacy Engine

Selected Publications [DBLP]

• "Kamino: Constraint-Aware Differentially Private Data Synthesis", with Chang Ge, Shubhankar Mohapatra, and Ihab F Ilyas, VLDB 2021, pdf
• "Catch a Blowfish Alive: A Demonstration of Policy-Aware Differential Privacy for Interactive Data Exploration", with Jiaxiang Liu, Karl Knopf, Yiqing Tan, and Bolin Ding, VLDB 2021, pdf
• "DPGraph: A Benchmark Platform for Differentially Private Graph Analysis", SIGMOD 2021 Demo, site
• "DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications", with Sameer Wagh, Ashwin Machanavajjhala, and Prateek Mittal, CACM 2021, link
• "SAQE: Practical PrivacyPreserving Approximate Query Processing for Data Federations", with Johes Bater, Yongjoo Park, Xiao Wang, and Jennie Rogers, VLDB 2020, pdf
• "Linear and Range Counting under Metric-based Local Differential Privacy", with Zhuolun Xiang, Bolin Ding, and Jingren Zhou, ISIT 2020, link
• "Computing Local Sensitivities of Counting Queries with Joins", with Yuchao, Sudeepa, and Ashwin, SIGMOD 2020, link
• "Crypte:crypto-Assisted Differential Privacy on Untrusted Servers", with Amrita Roy Chowdhury, Chenghong Wang, Ashwin Machanavajjhala, Somesh Jha, SIGMOD 2020, link
• "Towards Accuracy Aware Minimally Invasive Monitoring (MiM)", with Sameera Ghayyur, Dhrubajyoti Ghosh, and Sharad Mehrotra, TPDP 2019, link
• "PrivateSQL: A Differentially Private SQL Query Engine", with Ios Kotsogiannis, Yuchao Tao, Ashwin Machanavajjhala, Michael Hay and Gerome Miklau, VLDB 2019, link
• "Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing", with Changchang Liu, Thee Chanyaswad, Shiqiang Wang, Prateek Mitta, PETS 2019, link
• "Shrinkwrap: Differentially-Private Query Processing in Private Data Federations", with Johes, Ashwin, and Jennie, VLDB 2019, link
• "APEx: Accuracy-Aware Differentially Private Data Exploration", with Chang Ge, Ihab Ilyas, and Ashwin Machanavajjhala, SIGMOD 2019 link
• "Provably privacy for mobility data", with Ashwin Machanavajjhala, Springer Handbook on Mobile Data Privacy, 2018
• "Composing differential privacy and secure computation: A case study on scaling private record linkage", with Ashwin Machanavajjhala, Cheryl Flynn, and Divesh Srivastava, CCS 2017 link
• "A Demonstration of VisDPT: visual exploration of differentially private trajectories", with Nisarg Raval and Ashwin Machanavajjhala, VLDB 2016 (BEST DEMO AWARD) pdf
• "DPT: Differentially private trajectory synthesis using hierarchical reference systems", with Graham Cormode, Ashwin Machanavajjhala, Cecilia M. Procopiuc, and Divesh Srivastava, VLDB 2015 pdf
• "Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies", with Ashwin Machanavajjhala and Bolin Ding, SIGMOD 2014 pdf

Students

• Karl Knopf (PhD)
• Shubhankar Mohapatra (MS -> PhD)
• Shufan Zhang (MS -> PhD)
• Harry Sivasubramaniam (MS, graduated)
• Christian Covington (MS, co-supervised by Gautam Kamath, graduated)
• Jerry (Jiaxiang) Liu (BS, graduated)
• Clara Xi (BS, graduated)

Teaching

• CS848: Building Privacy-Aware Database Systems, Winter 2021
• CS848: Privacy & Fairness in Data Science, Fall 2019
• CS348: Introduction to Database System, Winter 2019, Winter 2020, Spring 2021, Fall 2022
• CS590.01: Privacy & Fairness in Data Science, co taught with Ashwin Machanavajjhala and Brandon Fain, Fall 2018
• Tutorial on "Practical Security and Privacy for Database System", SIGMOD 2021
• Tutorial on "Differential privacy in the wild: a tutorial on current practices & open challenges", (with Ashwin Machanavajjhala and Michael Hay), VLDB 2016 , SIGMOD 2017

BioSketch

• Ph.D.(Computer Science), Duke Univiersity, 2018.
• M.S.(Computer Science), Duke Univiersity, 2015.
• B.S.(Computer Science) & B.S.(Applied Mathematics), National University of Singapore, 2012