Xi He

News

• New paper accepted at SIGMOD'24: "DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance", (with Shufan Zhang), arXiv
• New article accepted at Statistica Sinica, "Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy", with Christian Covington, James Honaker, and Gautam Kamath, link
• 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]

• "DPrio: Efficient Differential Privacy with High Utility for Prio", with Dana Keeler (Mozilla Corporation), Chelsea Komlo (University of Waterloo, Zcash Foundation), Emily Lepert (University of Waterloo), Shannon Veitch (Eth Zürich), PoPETS 2023, link
• "Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration", with Miti Mazmudar, Thomas Humphries, Jiaxiang Liu, Matthew Rafuse, VLDB'23, link
• "Don't Be a Tattle-Tale: Preventing Leakages through Data Dependencies on Access Control Protected Data", with Primal Pappachan, Shufan Zhang, Sharad Mehrotra, VLDB'22, arXiv
• "MIDE: Accuracy Aware Minimally Invasive Data Exploration for Decision Support", with Sameera Ghayyur, Dhrubajyoti Ghosh, Sharad Mehrotra, VLDB'22, pdf
• "The Role of Adaptive Optimizers for Honest Private Hyperparameter Tuning", with Shubhankar Mohapatra, Sajin Sasy, Gautam Kamath, and Om Thakkar,AAAI'22, arXiv
• "Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases", with Priyanka Nanayakkara, Johes Bater, Jessica Hullman, and Jennie Rogers, PETS'22, arXiv demo
• "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
• "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
• "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
• "APEx: Accuracy-Aware Differentially Private Data Exploration", with Chang Ge, Ihab Ilyas, and Ashwin Machanavajjhala, SIGMOD 2019 link

Students & Postdocss

• Masoumeh Shafieinejad (Vector Postdoc)
• Karl Knopf (PhD)
• Shubhankar Mohapatra (MS -> PhD)
• Shufan Zhang (MS -> PhD)
• Wei Pang (MS)
• Calvin Wang (MS)
• Yingke Wang (MS, co-supervised by Sirisha Rambhatla)
• Harry Sivasubramaniam (MS, graduated)
• Christian Covington (MS, co-supervised by Gautam Kamath, graduated)
• Jerry (Jiaxiang) Liu (BS, graduated)
• Clara Xi (BS, graduated)

Teaching

• CS848: Privacy for Data Analysis and ML, Fall 2024
• 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