Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
PrivDNFIS: Privacy-preserving and Efficient Deep Neuro-Fuzzy Inference System
Authors: Hao Ren, Xiao Lan, Rui Tang, Xingshu Chen
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In comprehensive experimental results, Priv DNFIS demonstrates an approximately 1.9 to 4.4 times reduction in end-to-end time cost compared to the benchmark. Performance Evaluation Implementation settings. Experiments are conducted on a computing machine with Intel (R) Xeon (R) CPU E5-26800 @ 2.70GHz processor with 8 cores, 4 GB RAM storage, and Ubuntu 20.04 operation system. |
| Researcher Affiliation | Academia | Hao Ren 1 2 3, Xiao Lan 1 2 3, Rui Tang 1 2 3 *, Xingshu Chen 1 2 3 1 School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China. 2 Key Laboratory of Data Protection and Intelligent Management (Sichuan University), Ministry of Education, China. 3 Cyber Science Research Institute, Sichuan University, Chengdu, China. hao.ren, lanxiao, tangrscu, EMAIL |
| Pseudocode | Yes | Algorithm 1: Privately compute the hidden layer on CSB 1: Input: The LWE ciphertexts LCTω+ ,j, the private input values CT b d. The weight matrix M and the bias vector b. 2: Output: The LWE ciphertexts LCTρj of ρj for all j [l]. 3: for j [l] do 4: for i [n] do 5: m[i] M[i][j]. 6: end for 7: bm π(m); CTα Ct Pt Mul(CT b d, bm). 8: LCTα Extract(CTα, n). 9: LCTβ Ct Pt Add(LCTα, b[j]). 10: LCTρj Ct Ct Add(LCTβ, LCTω+ ,j). 11: end for 12: return a set of ciphertexts {LCTρj}, j [l]. |
| Open Source Code | No | The paper mentions using and adapting open-sourced code for third-party tools like SEAL and ciphertext extraction functions, but it does not provide an explicit statement or link for the open-source release of the Priv DNFIS methodology described in this paper. |
| Open Datasets | Yes | When processing 200 queries on CIFAR-100 (Krizhevsky, A.; Nair, V.; and Hinton, G 2013), Priv DNFIS takes 1194.62s in total. For two testing datasets CIFAR-10 and CIFAR-100, the accuracy of the non-private scheme DCNFIS (Yeganejou et al. 2023) and Priv DNFIS are the same. |
| Dataset Splits | Yes | For two testing datasets CIFAR-10 and CIFAR-100... The datasets CIFAR-10 and CIFAR-100 are well-known benchmark datasets that come with predefined standard training and testing splits, which are implicitly used for the experiments. |
| Hardware Specification | Yes | Experiments are conducted on a computing machine with Intel (R) Xeon (R) CPU E5-26800 @ 2.70GHz processor with 8 cores, 4 GB RAM storage, and Ubuntu 20.04 operation system. |
| Software Dependencies | No | The paper mentions 'Ubuntu 20.04 operation system' and using 'the RLWE/LWE FHE library SEAL (Laine, K.; Cruz, R.; Boemer, F.; Angelou, N.; and et al 2015)' without specifying the version number for SEAL. It does not list multiple key software components with their versions. |
| Experiment Setup | No | The paper describes the cryptographic parameters to ensure security (e.g., '128-bit security' for SEAL) and how different operations are performed in a privacy-preserving manner. However, it does not provide specific experimental setup details for training the DNFIS model, such as learning rates, batch sizes, number of epochs, or optimizer settings. |