مقاله انگلیسی رایگان در مورد به سوی یک سامانه توصیه‌گر برای حفظ قابل اطمینان حریم خصوصی – الزویر 2019

1- Introduction

2- Preliminaries

3- Methodology

4- Evaluation

5- Application to factorization machine

6- Conclusions and future work

References

Abstract

This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.

Introduction

Collaborative filtering (CF) is one of the most popular models for recommending items [27]. Its basic idea is to make recommendation based on the similarity between users or between items. CF-based models are trained by making use of user feedbacks on items. CF models can be divided into two categories of feedback: numerical and one-class. Numerical feedback consists of numeric values (e.g., ratings between 1 and 5). One-class feedback is a kind of record/action for a specific action (e.g., purchase an item or not). As the training process of CF-based models relies on large-scale data, service providers should collect a huge collection of user feedback records. However, if the servers are untrusted or contain vulnerabilities, the collection of user feedbacks may lead to privacy liability due to data leakage. Even if the servers are curious-but-honest, which means the services are functioning normally, feedback data leakage can still make private attributes and even real identity of users be inferred by hackers [7,10]. There are three categories of data leakage in CF-based recommenders. The first is Value Leakage: exposure of the values of feedbacks, such as the rating scores. The second is Existence Leakage: exposure of the existence of feedbacks, such as whether a user rates an item. For example, if the attacker knows that user u rates a book i, they can be quite certain that u has read i. The third is Model Leakage: exposure of the trained model. Models are important since given the CF model, attackers can estimate the ratings from any user to any item.