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.

CloudComputing‚Äč

 

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