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The PITS lab focuses on the impact and applications of probability theory and information theory to cybersecurity. The lab's cybersecurity scope is broad and cross-disciplinary, with applications spanning privacy metrics and practical privacy solutions, cyber-physical system security and applications to the power generation and distribution grid, secure information dissemination in social networks and social network user privacy and manipulation. Our work relies on skills from various domains of science and engineering, such as cyber security, machine learning, signal processing, information and probability theory, control theory, statistics and social sciences.

In the area of privacy, the PITS lab is investigating privacy metrics in the context of imperfect adversarial knowledge, as well as practical solutions to navigating the privacy-utility tradeoff in various contexts, including distributed multi-user frameworks and dynamic environments. Some of our solutions include casting and solving dynamic privacy-utility problems as Markov decision processes, designing variational-autoencoder-based privacy mechanisms, and investigating distributed multiuser privacy-utility tradeoffs as multi-player games.


To address the security of cyber-physical systems, the PITS lab is studying the impact of stealthy attacks -- including zero-dynamics attacks -- on cyber-physical systems, when the attacker, and possibly the controller, have imperfect information about the system model. Our solutions to system hardening against stealthy attacks include privacy mechanisms for thwarting the attacker's ability to closely learn and follow the target system's parameters.

For information dissemination in social networks, the PITS lab is conducting cross-disciplinary efforts to accurately model information-based user interaction in human networks, with the end goal of defining manipulation and differentiating it from legitimate influence. Such models can serve a variety of goals, from predicting viral trends to optimizing budget-constrained advertising, and to containing malicious misinformation.


In the past, the PITS lab has developed practical protocols for non-traditional key establishment based on common randomness harvested from networking metadata in ad-hoc wireless networks. As a part of the same effort, the lab developed efficient methods for calculating the theoretical bounds for the secret-key capacity of complex sources of randomness, representable as sibling hidden Markov processes.

In the area of biometric authentication, the PITS lab investigated computer-induced procedural biases as a means of enhancing the performance of established continuous biometric authentication algorithms.

In the area of physically-unclonable functions (PUFs), the PITS lab exposed for the first time the discharge inversion effect (DIE) in SRAM-based PUFs. If not well controlled for when learning the SRAM statistics necessary for fuzzy extraction, the DIE has the potential to cause catastrophic failure in authentication or randomness generation applications.

Abiola's graduation
Our beautiful campus
Our beautiful campus
Our beautiful campus
Successful fishing expedition
Our beautiful campus
Our beautiful campus
At the GSA picnic
Our beautiful campus
Our beautiful campus
Our beautiful campus
Group photo in the lab
PITS Lab Personnel

Adaeze Okeukwu

PhD student

Adaeze is a Phd student who joined the PITS lab in 2019. Her research interest centers around the intersection of machine learning and security, more specifically in network security. She obtained her bachelor’s degree In Mathematics and Computer Science from the Federal University of Technology Minna, Nigeria, and had worked as a Network Security Administrator for 4 years.

Bishwas Mandal

PhD Student

Bishwas is a PhD student who joined the PITS lab in 2020. He graduated with his B.S. degree in Computer Science from Koneru Lakshmaiah Education Foundation, India in August, 2019. His research interests are focused on Information Security and Privacy. He is currently working on Social Media Privacy.

Bipin Paudel

PhD student

Bipin is a PhD student who joined the PITS lab in 2021. He got his BS degree in Computer Science from Deerwalk Institute of Technology which is affiliated with the Tribhuvan University, Nepal, in early 2018. After that, he worked as a Software engineer before joining the PITS lab. He research interests are Security and Machine Learning

Niranjana Unnithan

PhD Student

Niranjana is a Ph.D. student who joined the PITS lab in 2023. She is interested in Machine Learning, Graphs and Privacy. She completed her master's degree in computer science from National University of Singapore. Prior to that, she worked as a software engineer with Ericsson India Global Services.

Vishnu Bondalakunta

PhD Student

Vishnu is a PhD student who joined the PITS lab in 2023. He got his Bachelor of Science(Research) degree in Mathematics from the Indian Institute of Science, India in May 2020. His research interest is privacy, particularly differential privacy and game theoretic models. Prior to that, he worked on projects in program verification and neural network verification.

PITS Lab Former Graduate Students

Stephanie Harshbarger

PhD, Graduated 2022

Dissertation: The impact of zero-dynamics stealthy attacks on control systems: stealthy attack success probability and attack prevention

Chandra Sharma

PhD, Graduated 2022

Dissertation: Towards optimal strategies for the management of online information and activity: privacy and utility tradeoffs

Abiola Osho

PhD, Graduated 2022

Dissertation: Privacy and security implications of active participation in online social networks: An information diffusion based approach to modeling user behavioral patterns

Shahnewaz Karim Sakib

PhD, Graduated 2023, Iowa State University

Dissertation: A General Framework of Estimating Information Leakage for Privacy and Forensics Problems with Imperfect Statistical Information

Joy Hauser

MS, Graduated 2021

Thesis: Automated malware analysis for Android applications through raw bytecode

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