Parameter-free HE-friendly Logistic Regression
Authors: Junyoung Byun, Woojin Lee, Jaewook Lee
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various real-world data show that our framework achieves better classification results while reducing latency by 68%, compared to the previous models. In this section, we evaluate our method using various real-world datasets. Through experiments, we argue that our method achieves better classification results compared to the existing methods with a shorter computation time. |
| Researcher Affiliation | Academia | Junyoung Byun Seoul National University Seoul, Korea quswns95@snu.ac.kr Woojin Lee Dongguk University-Seoul, Seoul, Korea wj926@dgu.ac.kr Jaewook Lee Seoul National University Seoul, Korea jaewook@snu.ac.kr |
| Pseudocode | Yes | Algorithm 1 Training Ridge Regression with Encrypted Private Variable |
| Open Source Code | No | The paper does not include an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We used five widely used classification datasets from the UCI data repository: The adult income dataset (Adult), bank marketing dataset (Bank), Wisconsin Breast Cancer dataset (Cancer), Pima Indians Diabetes dataset (Diabetes), and Australian Credit Approval (Credit) dataset [11]. |
| Dataset Splits | No | For each dataset, we randomly sampled 20% as test samples, and 20% of the other 80% were treated as plaintext data, which were used for the training of step 1. We encrypted the private variables of the remaining 60% and used them for steps 2 and 3. (Does not mention a distinct validation split). |
| Hardware Specification | Yes | All the experiments were performed on a machine equipped with 40 threads of an Intel Xeon E-2660 v3 @2.60GHz CPU processor. |
| Software Dependencies | Yes | We implemented step 1 of our framework with Python 3.6.3, using the LR module in the scikit-learn library. Other steps were implemented with C++, using HEAAN v1.1 [7] for HE. |
| Experiment Setup | Yes | For CKKS parameters, we used N = 216, q L = 21200, and P = 240. The sigmoid function approximation degree for LRHE was set to 3 because increasing the degree results in a larger multiplicative depth and less possible number of gradient descents with LHE. In addition, we observed that increasing the degree up to 7 did not significantly affect the performance of the model. The learning rate for LRHE was chosen in {0.001, 0.0001, 0.00001} to achieve the best classification performance. |