Private Secure Coded Computation

By Jungwoo Lee. 2019

43 citations recorded by the source corpus.

We introduce a variation of coded computation that protects the data security and the master’s privacy against the workers, which is referred to as private secure coded computation. In private secure coded computation, the master needs to compute a function of its own dataset and one of the datasets in a library exclusively shared by the external workers. After recovering the desired function, the workers should not know which dataset in the library was desired by the master or obtain any information about the master’s own data. We propose a private secure coded computation scheme for matrix multiplication, namely private secure polynomial codes, based on private polynomial codes for private coded computation. By simulation, we show that the private secure polynomial codes achieves better computation time than private polynomial codes modified for private secure coded computation.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Wei-Ting Chang
2. Ravi Tandon
3. Salim el Rouayheb

Citations in the corpus

• On the Upload versus Download Cost for Secure and Private Matrix Multiplication (2019, methodology): “Furthermore, there are several (U,D) pairs that the scheme in [12] cannot achieve, particularly for smaller values of upload cost.”