Jingbo Yan

University at Buffalo, State University of New York

Papers

Total Citations

145

H-Index

About

Jingbo Yan is a leading researcher in privacy-preserving cloud computing, with a primary focus on securing image and multimedia data processing. His seminal work, "Towards Efficient Privacy-preserving Image Feature Extraction in Cloud Computing" (2014, 119 citations), pioneered methods for outsourcing computationally intensive tasks like SIFT (Scale-Invariant Feature Transform) feature detection to cloud servers while maintaining data confidentiality. This foundational contribution addressed the critical challenge of leveraging cloud resources—such as economic computing and ubiquitous access—without compromising sensitive visual information. Yan further advanced this domain with "SecSIFT" (2016, 26 citations), refining secure protocols for local feature detection in object recognition and robotic mapping applications. His research directly responds to the explosive growth of image data from individuals and enterprises, offering practical solutions for secure cloud-based multimedia analytics. By enabling privacy-preserving feature extraction, Yan’s work has significant implications for fields ranging from autonomous systems to surveillance, ensuring that the benefits of cloud computing can be harnessed without sacrificing user privacy. His contributions remain highly influential in the intersection of cryptography, computer vision, and distributed systems.