Abstract:
For resource-constrained clients to save money, outsourcing complex, intensive tasks to public cloud vendors is inevitable. Public cloud vendors are usually untrustworthy. They may accidentally leak data, misuse data, compromise privacy, or corrupt computational results to make the system unreliable.
It’s crucial to prevent this while using public cloud vendors’ computational power. Large-scale data processing uses non-negative matrix factorization (NMF) for data dimension reduction. NMF’s non-polynomial hardness prevents local computation resources from efficiently processing big data.
Motivated by this issue, we present a novel outsourced NMF scheme (O-NMF) to reduce clients’ computing burden and address security issues. O-NMF uses Paillier homomorphism to protect data on two non-collusion servers. O-NMF also provides a high-probability verification mechanism. Security analysis and experimental evaluation show O-NMF’s validity and practicality.
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