In this paper, a new notion which we call private data deduplication protocol, a deduplication technique for private data storage is introduced and formalized. Intuitively, a private data deduplication protocol allows a client who holds a private data proves to a server who holds a summary string of the data that he/she is the owner of that data without revealing further information to the server. Our notion can be viewed as a complement of the state-of-the-art public data deduplication protocols of Halevi et al [7]. The security of private data deduplication protocols is formalized in the simulation-based framework in the context of two-party computations. A construction of private deduplication protocols based on the standard cryptographic assumptions is then presented and analyzed. We show that the proposed private data deduplication protocol is provably secure assuming that the underlying hash function is collision-resilient, the discrete logarithm is hard and the erasure coding algorithm can erasure up to α-fraction of the bits in the presence of malicious adversaries in the presence of malicious adversaries. To the best our knowledge this is the first deduplication protocol for private data storage.
This paper studies privacy-preserving weighted federated learning within the secret sharing framework, where individual private data is split into random shares which are distributed among a set of pre-dened computing servers. The contribution of this paper mainly comprises the following four-fold: • In the rst fold, the relationship between federated learning (FL) and multi-party computation (MPC) as well as that of secure federated learning (SFL) and secure multi-party computation (SMPC) is investigated. We show that FL is a subset of MPC from the m-ary functionality point of view. Furthermore, if the underlying FL instance privately computes the dened mary functionality in the simulation-based framework, then the simulation-based FL solution is an instance of SMPC. • In the second fold, a new notion which we call weighted federated learning (wFL) is introduced and formalized. Then an oracle-aided SMPC for computing wFL is presented and analysed by decoupling the security of FL from that of MPC. Our decoupling formulation of wFL benets FL developers selecting their best security practices from the state-of-the-art security tools. • In the third-fold, a concrete implementation of wFL leveraging the random splitting technique in the framework of the 3-party computation is presented and analysed. The security of our implementation is guaranteed by the security composition theorem within the secret share framework. • In the fourth-fold, a complement to MASCOT is introduced and formalized in the framework of SPDZ, where a novel solution to the Beaver triple generator is constructed from the standard El Gamal encryption. Our solution is formalized as a three-party computation and a generation of the Beaver triple requires roughly 5 invocations of the El Gamal encryptions. We are able to show that the proposed implementation is secure against honest-but-curious adversary assuming that the underlying El Gamal encryption is semantically secure.
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