A variant of LinUCB bandit algorithm with local differential privacy guarantee

Related tags

Data Analysis ldp-linucb
Overview

Contents

LDP LinUCB

Locally Differentially Private (LDP) LinUCB is a variant of LinUCB bandit algorithm with local differential privacy guarantee, which can preserve users' personal data with theoretical guarantee.

Paper: Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang. "Locally Differentially Private (Contextual) Bandits Learning." Advances in Neural Information Processing Systems. 2020.

Model Architecture

The server interacts with users in rounds. For a coming user, the server first transfers the current model parameters to the user. In the user side, the model chooses an action based on the user feature to play (e.g., choose a movie to recommend), and observes a reward (or loss) value from the user (e.g., rating of the movie). Then we perturb the data to be transferred by adding Gaussian noise. Finally, the server receives the perturbed data and updates the model. Details can be found in the original paper.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: MovieLens 100K

  • Dataset size：5MB, 100,000 ratings (1-5) from 943 users on 1682 movies.
  • Data format：csv/txt files

Environment Requirements

Script Description

Script and Sample Code

├── model_zoo
    ├── README.md                                // descriptions about all the models
    ├── research
        ├── rl
            ├── ldp_linucb
                ├── README.md                    // descriptions about LDP LinUCB
                ├── scripts
                │   ├── run_train_eval.sh        // shell script for running on Ascend
                ├── src
                │   ├── dataset.py               // dataset for movielens
                │   ├── linucb.py                // model
                ├── train_eval.py                // training script
                ├── result1.png                  // experimental result
                ├── result2.png                  // experimental result

Script Parameters

  • Parameters for preparing MovieLens 100K dataset

    'num_actions': 20         # number of candidate movies to be recommended
'rank_k': 20              # rank of rating matrix completion

  • Parameters for LDP LinUCB, MovieLens 100K dataset

    'epsilon': 8e5            # privacy parameter
'delta': 0.1              # privacy parameter
'alpha': 0.1              # failure probability
'iter_num': 1e6           # number of iterations

Launch

  • running on Ascend

    python train_eval.py > result.log 2>&1 &

The python command above will run in the background, you can view the results through the file result.log.

The regret value will be achieved as follows:

--> Step: 0, diff: 348.662, current_regret: 0.000, cumulative regret: 0.000
--> Step: 1, diff: 338.457, current_regret: 0.000, cumulative regret: 0.000
--> Step: 2, diff: 336.465, current_regret: 2.000, cumulative regret: 2.000
--> Step: 3, diff: 327.337, current_regret: 0.000, cumulative regret: 2.000
--> Step: 4, diff: 325.039, current_regret: 2.000, cumulative regret: 4.000
...

Model Description

The original paper assumes that the norm of user features is bounded by 1 and the norm of rating scores is bounded by 2. For the MovieLens dataset, we normalize rating scores to [-1,1]. Thus, we set sigma in Algorithm 5 to be $$4/epsilon * sqrt(2 * ln(1.25/delta))$$.

Performance

The performance for different privacy parameters:

  • x: number of iterations
  • y: cumulative regret

Result1

The performance compared with optimal non-private regret O(sqrt(T)):

  • x: number of iterations
  • y: cumulative regret divided by sqrt(T)

Result2

Description of Random Situation

In train_eval.py, we randomly sample a user at each round. We also add Gaussian noise to the date being transferred.

ModelZoo Homepage

Please check the official homepage.

You might also like...
Open source home automation that puts local control and privacy first
Open source home automation that puts local control and privacy first

Home Assistant Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiast

Home Assistant 57k Jan 2, 2023
Open source home automation that puts local control and privacy first.
Open source home automation that puts local control and privacy first.

Home Assistant Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiast

Home Assistant 57k Jan 1, 2023
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 3, 2023
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 915 Dec 29, 2022
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

A Minimalist Approach to Offline Reinforcement Learning TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weight

Scott Fujimoto 193 Dec 23, 2022
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

Jingyun Liang 139 Dec 29, 2022
A playable version of Chess – classic two-player, various AI levels, and the crazyhouse variant! Written in Python 3

A playable version of Chess – classic two-player, various AI levels, and the crazyhouse variant! Written in Python 3. Requires the installation of PIL/Pillow and Requests

null 1 Dec 24, 2021
Minimalistic generic chess variant GUI using pyffish and PySimpleGUI, based on the PySimpleGUI Chess Demo

FairyFishGUI Minimalistic generic chess variant GUI using pyffish and PySimpleGUI, based on the PySimpleGUI Chess Demo. Supports all chess variants su

Fabian Fichter 6 Dec 20, 2022
A variant caller for the GBA gene using WGS data

Gauchian: WGS-based GBA variant caller Gauchian is a targeted variant caller for the GBA gene based on a whole-genome sequencing (WGS) BAM file. Gauch

Illumina 16 Oct 13, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 3, 2023
Implementation of the Transformer variant proposed in
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022
Pipenv-local-deps-repro - Reproduction of a local transitive dependency on pipenv

Reproduction of the pipenv bug with transitive local dependencies. Clone this re

Lucas Duailibe 2 Jan 11, 2022
A simple python script to dump remote files through a local file read or local file inclusion web vulnerability.
A simple python script to dump remote files through a local file read or local file inclusion web vulnerability.

A simple python script to dump remote files through a local file read or local file inclusion web vulnerability. Features Dump a single file w

Podalirius 48 Dec 3, 2022
Official code for Score-Based Generative Modeling through Stochastic Differential Equations
Official code for Score-Based Generative Modeling through Stochastic Differential Equations

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Gen

Yang Song 818 Jan 6, 2023
Code for
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha

Winnie Xu 95 Nov 26, 2021
Supplementary code for the paper
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

null 536 Jan 5, 2023
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations

ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary

Somshubra Majumdar 15 Feb 10, 2022
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Beijing ColorfulClouds Technology Co.,Ltd. 16 Aug 7, 2022
Releases(v1.1.0)
Owner
Weiran Huang
Codes for papers
Weiran Huang
GitHub
Pipeline and Dataset helpers for complex algorithm evaluation.

tpcp - Tiny Pipelines for Complex Problems A generic way to build object-oriented datasets and algorithm pipelines and tools to evaluate them pip inst

Machine Learning and Data Analytics Lab FAU 3 Dec 7, 2022
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge Structural Database and the CoRE_MOF 2019 dataset.

null 1 Jan 9, 2022
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 3, 2023
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 1, 2023
Fully Automated YouTube Channel ▶️with Added Extra Features.

Fully Automated Youtube Channel ▒█▀▀█ █▀▀█ ▀▀█▀▀ ▀▀█▀▀ █░░█ █▀▀▄ █▀▀ █▀▀█ ▒█▀▀▄ █░░█ ░░█░░ ░▒█░░ █░░█ █▀▀▄ █▀▀ █▄▄▀ ▒█▄▄█ ▀▀▀▀ ░░▀░░ ░▒█░░ ░▀▀▀ ▀▀▀░

sam-sepiol 249 Jan 2, 2023
Privacy as Code for DSAR Orchestration: Privacy Request automation to fulfill GDPR, CCPA, and LGPD data subject requests.

Meet Fidesops: Privacy as Code for DSAR Orchestration A part of the greater Fides ecosystem. ⚡ Overview Fidesops (fee-dez-äps, combination of the Lati

Ethyca 44 Dec 6, 2022
Automated security testing using bandit and flake8.

flake8-bandit Automated security testing built right into your workflow! You already use flake8 to lint all your code for errors, ensure docstrings ar

Tyler Wince 96 Jan 1, 2023
Bandit is a tool designed to find common security issues in Python code.

A security linter from PyCQA Free software: Apache license Documentation: https://bandit.readthedocs.io/en/latest/ Source: https://github.com/PyCQA/ba

Python Code Quality Authority 4.8k Dec 31, 2022
banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's applied reinforcement learning platform, Reagent.

Bandit ML 51 Dec 22, 2022
"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback"

This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-decoder model and benchmarks the combination under simulated noisy rewards.

Khanh Nguyen 131 Oct 21, 2022