This research targets the design and evaluation of protocols for secure, privacy-preserving data analysis in an untrusted cloud. Therewith, the user can store and query data in the cloud, preserving privacy and integrity of outsourced data and queries. The PIs specifically address a real-world cloud framework: Google's prominent MapReduce paradigm.
Traditional solutions for single server setups and related work on, e.g., fully homomorphic encryption, are computationally too heavy and uneconomical and offset cloud advantages. The PIs' rationale is to design new protocols tailored to the specifics of the MapReduce computing paradigm. The PIs' methodology is twofold. First, the PIs design new protocols that allow the cloud user to specify data analysis queries for typical operations such as searching, pattern matching or counting. For this, the PIs extend privacy-preserving techniques, e.g., private information retrieval or order preserving encryption. Second, the PIs design protocols guaranteeing genuineness of data retrieved from the cloud. Using cryptographic accumulators, users can verify whether data has not been tampered with. Besides design, the PIs also implement a prototype that is usable in a realistic setting with MapReduce.
The outcome of this project enables privacy-preserving operations and secure data storage in a widely-used cloud computing framework, thus remove one major adoption obstacle, and make cloud computing available for a larger community.
