1,058 research outputs found
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
Recommendation is one of the critical applications that helps users find
information relevant to their interests. However, a malicious attacker can
infer users' private information via recommendations. Prior work obfuscates
user-item data before sharing it with recommendation system. This approach does
not explicitly address the quality of recommendation while performing data
obfuscation. Moreover, it cannot protect users against private-attribute
inference attacks based on recommendations. This work is the first attempt to
build a Recommendation with Attribute Protection (RAP) model which
simultaneously recommends relevant items and counters private-attribute
inference attacks. The key idea of our approach is to formulate this problem as
an adversarial learning problem with two main components: the private attribute
inference attacker, and the Bayesian personalized recommender. The attacker
seeks to infer users' private-attribute information according to their items
list and recommendations. The recommender aims to extract users' interests
while employing the attacker to regularize the recommendation process.
Experiments show that the proposed model both preserves the quality of
recommendation service and protects users against private-attribute inference
attacks.Comment: The Thirteenth ACM International Conference on Web Search and Data
Mining (WSDM 2020
- …