Innovative Cybersecurity Research Earns Assistant Professor Yuan Hong the NSF CAREER Award



By Casey Moffitt
A photo of Yuan Hong in front of a white background

Yuan Hong, assistant professor of computer science at Illinois Institute of Technology’s College of Computing, has received the National Science Foundation’s Faculty Early Career Development Program (CAREER) award for innovative techniques to perform security analytics that detect cybersecurity threats while preserving privacy.

To keep costs down, a fast-growing number of enterprises and organizations outsource their security analytics needs to external managed security service providers (MSSPs). Although cost effective and reliable, outsourcing requires these enterprises and organizations to share their large-scale and disparate datasets with MSSPs. Hong’s research tackles the privacy risks that enterprises and organizations take while outsourcing these services.

Hong’s research involves developing innovative tools that will allow a business to privately share their data, like company emails for example. These tools will allow a MSSP to ensure security, such as falling victim to a phishing scam, while protecting the privacy of the content of the business communications in the email example. MSSPs will be able to evaluate threats from the data without viewing proprietary information or identifying individuals from the data.

“Since security analytics should be conducted on huge amounts of data to detect threats, individuals’ privacy included in the outsourced data could be compromised,” Hong says. “The MSSP may learn private information of individuals employed by their clients. This research focuses on building tools to help MSSPs conduct security analyses without compromising their clients’ privacy.” 

The project aims to create a new paradigm of privacy-preserving data analysis to confidentially perform real-time threat detection. These analytic tools will work with both structured data such as relational databases and network traffic, and unstructured data such as surveillance videos, emails, and business documents. The main goal is to fundamentally advance privacy preservation while outsourcing cybersecurity analysis.

“This project technically bridges security, privacy, and machine learning,” Hong says. “Apart from protecting the sensitive organizational data in security analytics, the project will address personal privacy concerns of employees, such as their web browsing history, emails, and faces in the surveillance videos, while ensuring the compliance of privacy laws and regulations in this big data era.”

Hong says the challenges include analyzing large volumes of data generated in real time, bounding the privacy leakage in such big data, ensuring efficiency for real-time private analysis, and ensuring accuracy for threat detection while preserving privacy. However, with progress made in cryptographic protocol and differential privacy mechanism design in other domains, such as machine learning, smart grids, intelligent transportation systems, and search engine query analyses, Hong and his team will explore novel privacy solutions in this new project.

The project also includes a comprehensive educational and outreach program, including cybersecurity workforce training, educational materials development and distribution, K–12 outreach, and research dissemination to broader communities.

The prestigious NSF CAREER award of $499,996 acknowledges Hong’s contributions to data privacy, security analytics, and artificial intelligence security.

Photo: Assistant Professor of Computer Science Yuan Hong