Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Authors: Jinming Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Wang Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on two benchmark datasets demonstrate that S3Rec improves the computation time and communication size of the state-of-the-art model by about 40 and 423 in average, respectively. |
| Researcher Affiliation | Collaboration | Jamie Cui1, Chaochao Chen2,1*, Lingjuan Lyu3, Carl Yang4, and Li Wang1 1Ant Group 2Zhejiang University 3Sony AI 4Emory University |
| Pseudocode | Yes | Figure 1: Secure matrix multiplication protocol, where Shr is a secret sharing algorithm. ... Figure 3: Our proposed S3Rec framework, where Matrix Mul stands for secure matrix multiplication protocol, Add stands for secure add protocol, Rec stands for reconstruction protocol for secret sharing. ... Figure 5: Dense-sparse Matrix Mul(X, Y) with insensitive and sensitive sparsity protocols, where we have X Rk m, Y Rm m. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of its own source code for the methodology. |
| Open Datasets | Yes | Dataset. We choose two popular benchmark datasets to evaluate the performance of our proposed model, i.e., Epinions [19] and Library Thing (Lthing) [32], both of which are popularly used for evaluating social recommendation tasks. |
| Dataset Splits | Yes | We use five-fold cross-validation during experiments. |
| Hardware Specification | Yes | We run our experiments on a machine with 4-Core 2.4GHz Intel Core i5 with 16G memory, we compile our program using a modern C++ compiler (with support for C++ standard 17). |
| Software Dependencies | Yes | For additive HE scheme, we choose the implementation of libpaillier1. Also, we use Seal-PIR2 with same parameter setting as the original paper [1]. For security, we choose 128-bit computational security and 40-bit statistical security as recommended by NIST [2]. Similarly we leverage the generic ABY library3 to implement Se So Rec [5] and MPC building blocks such as addition, multiplication, and truncation. In particular, we choose 64-bit secret sharing in all our experiments. |
| Experiment Setup | Yes | Hyper-parameters. For all the model, during comparison, we set k = 10. We tune learning rate θ and regularizer parameter λ in {10 3, 10 2, ..., 101} to achieve their best values. We also report the effect of K on model performance. |