Privacy-Preserving Access, Search, and Computation of Encrypted Data in the Cloud
Objectives
Design a system architecture for access, search, and computation of encrypted data in the cloud under a new security threat model.
Study techniques for efficient access and search of encrypted data in the cloud.
Study techniques for efficient outsourced computation of encrypted data in the cloud.
Develop software toolkits and proof of concept demonstrations.
Existing Solutions and Their Limitations
Traditional access control models assume the servers are fully trusted and hence not suitable in heterogeneous computing environments such as the cloud.
Fully homomorphic encryption (FHE) allows a cloud server to perform computation on encrypted data but with huge overhead.
Outcomes/Deliverables
Mid-term: System design and implementation for access and search of encrypted data in the cloud.
Final: System design and implementation for secure outsourced computation of encrypted data in the cloud.
Practical Applications and Impact
Secure cloud data storage
Data localization and access control
Privacy-preserved machine learning
System Architecture/Description
Cloud service provider is assumed to be honest-but-curious, i.e., provides storage and computation services honestly but is persistently interested in learning users' sensitive information.
End-to-end data privacy protection for data users.
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