About

Abhishek Jain is an Associate Professor in Computer Science at Johns Hopkins University. His research broadly focuses on cryptography, computer security, privacy, and related topics in theoretical computer science. He co-leads the Cryptography Group and is a member of both the Theory Group and the Information Security Institute at Johns Hopkins University. His research has received generous support from various organizations including NSF Career, DARPA, JP Morgan, Ethereum Foundation, Stellar, Cisco, Samsung, and JHU Catalyst awards. As of Fall 2023, he also holds the position of Senior Scientist in the CIS Lab at NTT Research. He is actively seeking interns and post-doctoral researchers to work in Sunnyvale.

Research topics

• Computer Science
• Computer Security
• Theoretical computer science
• Discrete mathematics
• Algorithm
• Programming language
• Mathematics

Selected publications

• Quantum Computing Approaches for High-Speed Visual Search

  2025-05-29 · 1 citations

  articleSenior author

  This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100-150 ~\text{ms}$</tex>. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.

  PublisherDOI

• Interpretable AI Models for IoT-Enabled Environmental Monitoring and Disaster Risk Forecasting

  2025-07-25

  article

  The increasing frequency and severity of environmental disasters necessitate advanced methodologies to predict and mitigate their risks effectively. This paper introduces an interpretable AI framework tailored for forecasting environmental disaster risks using data from IoT-enabled sensors distributed across diverse geographies. The study leverages a comprehensive dataset containing key climate, soil, and air parameters sampled hourly over six months. Our methodology encompasses the use of interpretable machine learning models such as SHAP and LIME, which elucidate predictions by providing feature attribution, thus enhancing transparency in decision-making processes crucial during high-risk scenarios. Results indicate that incorporating explainability mechanisms significantly improves model trustworthiness without compromising prediction accuracy. Notably, the proposed models achieve high metrics, including accuracy, macro f1-score, and consistency of SHAP scores across trials. These findings underscore the potential of explainable AI to bolster environmental monitoring systems, ensuring informed decisions that can mitigate disaster impacts effectively while fostering trust among stakeholders.

  PublisherDOI

• Black-Box Non-interactive Zero Knowledge from Vector Trapdoor Hash

  Lecture notes in computer science · 2025-01-01 · 4 citations

  book-chapter
  PublisherDOI

• Obfuscating Pseudorandom Functions is Post-quantum Complete

  Lecture notes in computer science · 2025-12-01

  book-chapterOpen access
  PublisherOA PDFDOI

• Abnormality detection for IoT devices

  AIP conference proceedings · 2025-01-01

  article
  PublisherDOI

• Simultaneous-Message and Succinct Secure Computation

  Lecture notes in computer science · 2025-01-01 · 4 citations

  book-chapter
  PublisherDOI

• Fully Anonymous Secret Sharing

  Lecture notes in computer science · 2025-01-01 · 4 citations

  book-chapter
  PublisherDOI

• &lt;p&gt;Cybersecurity Risks In 5G Networks: Strategies for Safeguarding Next-Generation Communication Systems&lt;/p&gt;

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Leveraging Deep Learning for Lip Reading: A Comprehensive Analysis

  Lecture notes in networks and systems · 2025-01-01

  book-chapter
  PublisherDOI

• Multi-Key Homomorphic Secret Sharing

  Lecture notes in computer science · 2025-01-01 · 8 citations

  book-chapterOpen access
  PublisherOA PDFDOI

Recent grants

• SaTC: CORE: Small: Secure Computation with Minimal Interaction

  NSF · $474k · 2018–2023

• CAREER: New Frontiers in Computing on Private Data

  NSF · $576k · 2020–2026

Frequent coauthors

• Sanjam Garg

  53 shared

• Vipul Goyal

  Regional Cancer Center, Thiruvananthapuram

  43 shared

• Vinod Vaikuntanathan

  42 shared

• Nir Bitansky

  39 shared

• Justin Holmgren

  39 shared

• Sidharth Telang

  Johns Hopkins University

  36 shared

• Rafael Pass

  36 shared

• Amit Sahai

  31 shared

Labs

• Advanced Research in CryptographyPI

  Not provided

Awards & honors

• NSF CAREER Award (2020)
• Best Paper Awards at Eurocrypt
• Symantec Outstanding Graduate Student Award