Welcome to Data Security and Privacy Lab (DSP-Lab, founded in 2017) in the Department of Computer Science and Engineering at the University of Notre Dame. Here at DSP-Lab, we aim to explore and resolve security and privacy issues existing in the world of data so that people will be less involved in security or privacy breach in the big data era. The research areas of DSP-Lab at Notre Dame spans widely across multiple areas, from data science research to traditional security research, owing to the interdisciplinary nature of the security and privacy research in the big data.

Current research areas

The following sections describe our current on-going research projects. Please contact Prof. Taeho Jung if you want further discussion.

Secure and accountable management of big data

More and more data are generated and collected nowadays, but we do not have a way to monitor and control the management of those data. We study how to let individuals hold ultimate controls over their own personal data being collected everyday and everywhere.

Keywords: Accountability, data provisioning, secure provenance

Relevant software: Secure fuzzy deduplication on images

Privacy-preserving deep learning

Deep learning technologies have given birth to numerous innovative applications in our life. We believe now it is a proper time to consider the user privacy implications behind this breakthrough technology. In this project, we study how to enable various deep learning technologies without breaching individual privacy.

Keywords: Privacy-preserving computation, applied cryptography, secure multi-party computation, differential privacy

Relevant software: One-round secure multiparty computation with TPMModified HEtest framework for testing and comparing SEAL/HElib.

On scalability and maintenance cost of blockchain

Blockchain has various desirable security properties (e.g., tamper-proofness, decentralization), however it has several shortcomings as well. Our goal is to make blockchain more scalable and sustainable.

Keywords: Blockchain, efficiency, scalability

Relevant software: Blockchain with proof of deep learning


Current members

Taeho Jung
Ryan Karl
Changhao Chenli
Jonathan Takeshita

Past members

Timothy Burchfield
Cian Levy








We have been actively collaborating with the following groups/labs.

Some Photos

At a group meeting


While attending ACM CCS 2018 at Toronto