Current Research Projects

Research in IST cuts across traditional boundaries to drive interdisciplinary discovery and innovation. Our research is sponsored by a variety of national and international agencies, and we collaborate with diverse groups of scholars within and beyond Penn State. Explore our funded projects to see how IST's transformative research is addressing the world's most complex problems at the intersection of information, technology, and society.

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Current Projects

Researcher:
Wu, Dinghao
Sponsoring Agency: Office of Naval Research
We are considering a radically different approach to binary reverse engineering tools by placing the recompilability as the first and topmost goal. We will further develop our preliminary study on Reassembleable Disassembling, with the similar design goal to preserve the recompilability while lifting the code to higher level languages or intermediate representations.   Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Squicciarini, Anna
Sponsoring Agency: Penn State Center for Security Research and Education
Transportation networks are part of our critical infrastructure. Location data from vehicles on these networks is important for making infrastructure policies, studying congestion, reducing pollution, real time routing, and providing value added services (e.g., Uber, Lyft). However, this location data could lead to inferring information that users might consider private such as their residential locations, health and economic status, and religious affiliation. Obfuscation mechanisms have been proposed as effective ways to protect the privacy of individual users while sharing their locations. However, users have limited control over the amount of privacy on their data as they have no easy way to interact with obfuscation mechanisms which are either too rigid (with their parameters) or too naive (with their assumptions) to be truly effective. In this seed project, we propose to develop a policy-based framework for adaptive location obfuscation. Our framework will provide users with strong privacy guarantees while effectively allowing them to balance the tradeoff between utility and privacy, depending upon their needs. Thus, it will improve security and privacy of data sharing in services and critical infrastructure such as traffic flow networks. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Wilson, Shomir
Sponsoring Agency: National Science Foundation
This multi-disciplinary project aims to develop novel technology that will enable people to regain a sense of control by enabling them to simply ask questions about the privacy issues that matter to them rather than requiring them to read long, one-size-fits all privacy policies. This multi-disciplinary project aims to re-invent notice and choice, moving from long and hard-to-understand notices to interactive privacy dialogues with users. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Song, Linhai
Sponsoring Agency: Web3 Foundation
In this project, we will build an IDE tool for visualizing the lifetime scope of a user-selected Rust variable. We believe our tool can help Rust programmers avoid deadlocks at the development stage. After writing a piece of code involving a mutex, a programmer can select the return value of a locking operation or the locking operation itself (when the return is not saved to a variable). Our tool will visualize the lifetime scope of the return value (i.e., the critical section). The programmer can then inspect whether the end of the critical section is expected. In addition, our tool will conduct deadlock detection for the selected critical section and provide detailed debugging information for identified bugs, such as highlighting blocking operations or function calls leading to blocking operations. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Chen, Jinghui
Sponsoring Agency: Cisco Systems, Inc.
This project proposes to understand this adversarial robustness in anomaly detection via devising algorithms that could exploit this weakness and develop principled ways to mitigate the potential risk. It also proposes solutions for handling discrete or categorical data which is common in real-world applications. The proposed research could lead to much more reliable anomaly detection algorithms for cybersecurity in practice. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Chen, Jinghui
Sponsoring Agency: Cisco Systems, Inc.
This project proposes to understand this adversarial robustness in anomaly detection via devising algorithms that could exploit this weakness and develop principled ways to mitigate the potential risk. It also proposes solutions for handling discrete or categorical data which is common in real-world applications. The proposed research could lead to much more reliable anomaly detection algorithms for cybersecurity in practice. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Lee, Dongwon
Sponsoring Agency: National Science Foundation
This collaborative project between Penn State and Tuskegee University proposes to improve the solutions for the intrusion detection tasks by means of the Anomaly Detection framework in cyber systems by incorporating recent advancements in big data and machine learning techniques. In this project, we explore how to advance existing Anomaly Detection Systems (ADSs) to prevent more diverse and challenging types of network intrusions with higher detection accuracies. Recent advances in big data and machine learning, especially deep learning, provide an unprecedented opportunity for building highly effective ADSs. Therefore, the team will investigate methods in various data science and machine learning fields, and seek to exploit them in the context of network intrusion detection. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Wang, Ting
Sponsoring Agency: National Science Foundation
We propose a new disciplinary concept of computational Screening and Surveillance (CSS) that utilizes edge learning to collect, analyze and interpret both physical and physiologic data of human subjects, to detect early indicators of diseases, and monitor health changes in both individuals and populations. Real-time information, knowledge, and insights from extreme-scale CSS will revolutionize our understanding, prediction, intervention, treatment, and management of acute/infectious (e.g. flu, COVID), chronic physical and psychological/psychiatric diseases, resulting in huge savings for numerous diseases each costing hundreds of billion dollars every year. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Hills, Michael; Giacobe, Nicklaus A.
Sponsoring Agency: National Security Agency
The CYSP program provides scholarship opportunities to College of IST students interested in pursuing employment with the Department of Defense. It also provides faculty with the opportunity to compete for capacity building project funds to develop educational products of general use to the wider Center for Academic Excellence community. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Hills, Michael; Giacobe, Nicklaus A.
Sponsoring Agency: National Security Agency
The CYSP program provides scholarship opportunities to College of IST students interested in pursuing employment with the Department of Defense. It also provides faculty with the opportunity to compete for capacity building project funds to develop educational products of general use to the wider Center for Academic Excellence community. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Lee, Dongwon
Sponsoring Agency: National Science Foundation
In this project, we propose a flexible framework, named as SAGA, where scholars can easily create cybersecurity case studies (similar to business case studies) that have AI components. Further, by adopting the notion of “citation” in academic world and implementing it using public platforms (e.g., arXiv, Github, Kaggle), SAGA enables the developed case studies to be easily found and shared in the community, and the authors of case studies to be rightfully attributed for their efforts, thereby encouraging more participation from scholars in creating and sharing case studies. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Lee, Dongwon; Wang, Suhang
Sponsoring Agency: National Science Foundation
This project explores effective labeled data generation via generative adversarial learning and proposes novel approaches based on generative adversarial learning for effective labeled data generation to facilitate deep learning with limited label information, investigates associated fundamental research issues and develops effective algorithms. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher:
Lee, Dongwon; Wang, Suhang
Sponsoring Agency: National Science Foundation
This project explores effective labeled data generation via generative adversarial learning and proposes novel approaches based on generative adversarial learning for effective labeled data generation to facilitate deep learning with limited label information, investigates associated fundamental research issues and develops effective algorithms. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher:
Song, Linhai
Sponsoring Agency: Ethereum Foundation
In this work, we will extend GCatch to GCatch++, by enhancing it with the capability of detecting channelrelated non-blocking bugs. By pinpointing more concurrency bugs in Go programs, GCatch++ can help programmers resolve those bugs and further improve the reliability of the Go programs. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Squicciarini, Anna
Sponsoring Agency: National Science Foundation
This project investigates the complex chaotic behaviors that can emerge as a result of evolutionary dynamics on networks, develops techniques for dynamic control, and studies the problems associated with privacy and fairness among agents in these systems. The work focuses on semi-autonomous agents that interact with each other in a network and alter their instantaneous mixed strategies through evolutionary dynamics, with an emphasis on flocking and consensus dynamics. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Squicciarini, Anna
Sponsoring Agency: National Science Foundation
This project aims to build mathematical and data-driven models to understand the dynamics of extremist groups at scale, the patterns of their influence, and integrated micro (individual-level) and macro (group-level or system-level) data-driven models that can guide future interventions. This project provides a greater understanding of users' behavioral patterns and social dynamics related to online extremism. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher:
Chen, Jinghui
Sponsoring Agency: College of IST
This project seeks to new understandings of the vulnerabilities of distillation based federated learning – a machine learning technique, where local clients collaboratively train a global model without sharing clients' original data – and help build more robust federated learning methods for security-critical applications. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Wang, Ting
Sponsoring Agency: International Business Machine Corporation
The goal of Hardening and Orchestrating Responses Under Stress (HORUS) project is to enable dynamic response and adaptive protection for cyber hunting scenarios, leveraging (1) a cognitive threat analysis process to choose from effective protective responses and actions, (2) a game-theoretic model for action selection and attack anticipation, and (3) adversarial analysis of threats and hardening of detectors. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Squicciarini, Anna; Lee, Dongwon
Sponsoring Agency: National Science Foundation
This project will expand the capability and involvement of Penn State students State in cyber-relevant disciplines. To support student needs, we have implemented a flexible and strong Scholarship for Service (SFS) program, based on customized mentoring for each student. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Squicciarini, Anna; Lee, Dongwon
Sponsoring Agency: National Science Foundation
This project will expand the capability and involvement of Penn State students State in cyber-relevant disciplines. To support student needs, we have implemented a flexible and strong Scholarship for Service (SFS) program, based on customized mentoring for each student. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Squicciarini, Anna
Sponsoring Agency: National Science Foundation
This research project seeks to address privacy issues by developing a new location privacy framework for workers in vehicle-based spatial crowdsourcing. The project will start with developing new adversarial models to capture the network-constrained mobility features of multiple vehicles operating over roads. As a countermeasure of the adversarial models, the project will develop a new location obfuscation paradigm that can effectively protect vehicles' location privacy even assuming that adversaries use vehicles' mobility features for inference attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Giles, C. Lee; Wilson, Shomir
Sponsoring Agency: National Science Foundation
We propose to build a large-scale, longitudinal, annotated, and searchable resource of privacy policies, terms of service agreements, cookie policies, and other related documents for the privacy research community. This resource, which we name PrivaSeer, will serve three simultaneous roles: (1) a search engine for privacy documents (i.e., privacy policies plus other species of relevant text); (2) a source of corpora for use by the research community; and (3) an API for privacy-enhancing technologies to draw privacy information from on demand. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Giles, C. Lee; Wilson, Shomir
Sponsoring Agency: National Science Foundation
We propose to build a large-scale, longitudinal, annotated, and searchable resource of privacy policies, terms of service agreements, cookie policies, and other related documents for the privacy research community. This resource, which we name PrivaSeer, will serve three simultaneous roles: (1) a search engine for privacy documents (i.e., privacy policies plus other species of relevant text); (2) a source of corpora for use by the research community; and (3) an API for privacy-enhancing technologies to draw privacy information from on demand. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Xiong, Aiping; Lee, Dongwon
Sponsoring Agency: National Science Foundation
This funding establishes a new Research Experiences for Undergraduates (REU) Site at Pennsylvania State University. An interdisciplinary team of experienced faculty mentors will guide undergraduate students in summer research projects focused on applying machine learning methods to solve cybersecurity problems, particularly cyber-attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher:
Xiong, Aiping; Lee, Dongwon
Sponsoring Agency: National Science Foundation
This funding establishes a new Research Experiences for Undergraduates (REU) Site at Pennsylvania State University. An interdisciplinary team of experienced faculty mentors will guide undergraduate students in summer research projects focused on applying machine learning methods to solve cybersecurity problems, particularly cyber-attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher:
Hu, Hong
Sponsoring Agency: College of IST
This project aims to better protect popular document-processing software applications by testing the binding layers in scripting languages used to simplify file modification. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher:
Wang, Ting
Sponsoring Agency: National Science Foundation
This project aims at understanding the security threats incurred by reusing third-party models as building blocks of machine learning (ML) systems and developing tools to help developers mitigate such threats throughout the lifecycle of ML systems. Outcomes from the project will improve ML security in applications from self-driving cars to authentication in the short term while promoting more principled practices of building and operating ML systems in the long run. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Song, Linhai
Sponsoring Agency: National Science Foundation
This project aims to better understand Rust’s memory bugs and build novel static/dynamic tools to combat Rust’s memory bugs. This project contains three components: (1) a comprehensive taxonomy of Rust’s memory bugs, (2) novel static techniques to identify memory bugs in interior unsafe functions, and (3) novel fuzzing techniques enhanced by the safe/unsafe information in Rust. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Wang, Ting
Sponsoring Agency: National Science Foundation
The transformative nature of this project is to completely rethink how to define and implement the interpretation of DNNs and how to exploit this interpretability as a bridge to understand and control the DNN behaviors. This project aims to develop RIDDLE, a new interpretable deep learning framework that is reliable, interactive, and debuggable. Learn more...
Research Areas: Privacy and Security
Term: -