Prateek Mittal

About

Prateek Mittal is a Professor of Electrical and Computer Engineering at Princeton University, where he also serves as Associate Chair of the Department and Director of Undergraduate Studies. His research broadly focuses on privacy-preserving and secure systems, with current emphasis on security and machine learning, privacy and machine learning, and the intersection of privacy, security, and networked systems. His work draws on techniques from data science, network science, distributed systems, and applied cryptography. Professor Mittal's research has contributed to the design of widely-used systems such as the Tor network for anonymous communication and the Let's Encrypt Certificate Authority. He holds a Ph.D. and M.S. from the University of Illinois at Urbana-Champaign and a B.Tech. from the Indian Institute of Technology. He is an associated faculty member in the Center for Information Technology Policy and in computer science, recognized for his contributions to the field through numerous awards and honors, including the NSF CAREER Award, IEEE and ACM senior memberships, and the IBM Faculty Award. His work has been featured in various prestigious conferences and symposia, and he is known for his influence on security and privacy in networked systems.

Research topics

• Computer Security
• Computer Science
• Data Mining
• Data science
• Computer network
• Human–computer interaction
• Distributed computing

Selected publications

• Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning

  DOAJ (DOAJ: Directory of Open Access Journals) · 2021-01-01 · 221 citations

  article

  We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical…

  PublisherDOI

• Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence

  2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2021 · 117 citations

  Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised…

  DOI

• Visual Adversarial Examples Jailbreak Aligned Large Language Models

  Proceedings of the AAAI Conference on Artificial Intelligence · 2024-03-24 · 94 citations

  articleOpen accessSenior author

  Warning: this paper contains data, prompts, and model outputs that are offensive in nature. Recently, there has been a surge of interest in integrating vision into Large Language Models (LLMs), exemplified by Visual Language Models (VLMs) such as Flamingo and GPT-4. This paper sheds light on the security and safety implications of this trend. First, we underscore that the continuous and high-dimensional nature of the visual input makes it a weak link against adversarial attacks, representing an…

  PublisherOA PDFDOI

• Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters

  IEEE Transactions on Power Electronics · 2024-05-13 · 30 citations

  articleOpen access

  It is envisioned that the future electric grid will be underpinned by a vast number of smart inverters linking renewables at the grid edge. These inverters' dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may vary widely and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture…

  PublisherOA PDFDOI

• Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures

  IEEE Open Journal of the Computer Society · 2020 · 24 citations

  In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction. We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous groun…

  DOI

Recent grants

• SaTC: CORE: Medium: Collaborative: A Linguistically-Informed Approach for Measuring and Circumventing Internet Censorship

  NSF · $540k · 2017–2021

• TWC: Medium: Collaborative: Aspire: Leveraging Automated Synthesis Technologies for Enhancing System Security

  NSF · $400k · 2014–2018

• CAREER: Trustworthy Social Systems Using Network Science

  NSF · $521k · 2016–2022

Frequent coauthors

• Vikash Sehwag

  34 shared

• Arjun Nitin Bhagoji

  University of Chicago

  32 shared

• Mung Chiang

  31 shared

• Nikita Borisov

  25 shared

• Daniel Cullina

  24 shared

• Saeed Mahloujifar

  22 shared

• Shouling Ji

  Zhejiang University

  21 shared

• Peng Gao

  19 shared

Labs

• Prateek Mittal LabPI

Awards & honors

• CSAW Applied Research Finalist, 2018
• Army Research Office (ARO) Young Investigator Award, 2018
• Office of Naval Research (ONR) Young Investigator Award, 201…
• Princeton Engineering Commendation List for Outstanding Teac…
• IBM Faculty Award, 2017

Similar researchers at Princeton University

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• Paul Seymourh-75
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