Chenghong Wang

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Assistant Professor
Department of Computer Science
Indiana University Bloomington
3054 Luddy Hall, Bloomington, IN 47408
Contact: cw166 AT iu (dOt) edu

I am an Assistant Professor in the Department of Computer Science at Indiana University. I currently serve as the co-PI of the NSF Center for Distributed Confidential Computing (CDCC). I am also affiliated with Luddy’s Security & Privacy in Informatics, Computing, and Engineering (SPICE) Center. I received my Ph.D. in Computer Science from Duke University under the supervision of Ashwin Machanavajjhala and Kartik Nayak.

I am looking for Ph.D. students to join our team and contribute to confidential computing research in both system and architecture. Please drop me an email if you’re interested

Research

My current research focuses on trustworthy, data-centric AI infrastructures. Some current research projects are:

  • Private Data systems
    • ParsecDB (On-going): A highly efficient, full-fledged secure multi-party database for confidential analytics and ETL pipelines.
    • DPAR (SC25): A drop-in Message Passing Interface (MPI) AllReduce replacement that provides strong privacy with minimal performance cost.
    • SPECIAL (VLDB24): The first secure workload planner designed for complex analytics over private data federations.
    • DPidx: The first private learned index for encrypted federations.
    • Private PoS (Security23): Use PAC-based learning tools, such as noisy binary trees and Bayesian learners, to quantify privacy leakages in PoS blockchains.
  • Formal methods for data governance
    • Picachv (Security25): A novel security monitor that formally and automatically enforces data use policies within data analytics and ML data pipelines.
  • Confidential accelerators
    • BOLT (CCS25): A high-performance, secure, and data-oblivious KVS accelerator (the foundation for secure retrieval-based AI).
    • LinGCN (NeurIPS23):An accelerator architecture designed to reduce multiplication depth of homomorphic encryption based GCN inference.
    • AQ2PNN (MICRO23): An ultra-fast 2PC-DNN accelerator built on FPGAs.

My prior research focuses on the synergy of multi-party comptuation (MPC) and differential privacy (DP) to build practical encrypted databases SIGMOD20, SIGMOD21, SIGMOD22.

My research has received generous support from the NSF (2419821, 2207231), IU IAS, Intel, and AMD

Students

Ph.D

Alumni