Efficient Private Empirical Risk Minimization for High-dimensional Learning
Authors: Shiva Prasad Kasiviswanathan, Hongxia Jin
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we theoretically study the problem of differentially private empirical risk minimization in the projected subspace (compressed domain). |
| Researcher Affiliation | Industry | Shiva Prasad Kasiviswanathan KASIVISW@GMAIL.COM Samsung Research America, Mountain View, CA 94043 Hongxia Jin HONGXIA.JIN@SAMSUNG.COM Samsung Research America, Mountain View, CA 94043 |
| Pseudocode | Yes | Mechanism 1 PROJERM: Input: A random subgaussian matrix Φ Rm d, and a dataset D = (Φx1, y1), . . . , (Φxn, yn) of n datapoints from the domain MΦ = {(Φx, y) : x Rd, x 1, y R, |y| 1} Output: θpriv a differentially private estimate of ˆθ argminθ C 1 n Pn i=1 ℓ( xi, θ ; yi) 1. Let ϑpriv Output of an (ϵ, δ)-differentially private or an ϵ-differentially private ERM algorithm solving the following problem: argminϑ ΦC 1 n i=1 ℓ( Φxi, ϑ ; yi) 2. θpriv argminθ Rd θ C subject to Φθ = ϑpriv (can be solved with any convex programming technique) 3. Return: θpriv |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses abstract datasets with 'n datapoints' without referring to any specific, publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups, hence no dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for it. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software implementations or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |