S3BD: Secure Semantic Search over Encrypted Big Data in the Cloud
Jason Woodworth, Mohsen Amini Salehi

TL;DR
This paper introduces S3BD, a system enabling real-time semantic search over encrypted big data in the cloud, balancing data privacy with efficient search capabilities through data clustering and client-side abstraction.
Contribution
The paper presents a novel approach to secure, real-time semantic search over encrypted big data in the cloud using data clustering and client-side cluster abstraction.
Findings
Real-time search achieved on encrypted big data
Significant efficiency improvements over existing methods
Prototype implementation demonstrates practical viability
Abstract
Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client-end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real-time searching over the encrypted big data in the cloud. In particular, semantic search is of interest to clients dealing with big data. To address this problem, in this research, we develop a system (termed S3BD) for searching big data using cloud services without exposing any data to cloud providers. To keep real-time response on big data,…
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Taxonomy
TopicsCloud Data Security Solutions · Cryptography and Data Security · Privacy-Preserving Technologies in Data
