Privacy-Preserving Selective Aggregation of Online User Behavior Data

Abstract

The privacy of online users is therefore at risk of exposure to third parties.Over the past decade, a body of research work has been witnessing attempts to perform data aggregation in a way that protects privacy.

Most of the existing methods guarantee strong protection of privacy at the cost of very limited aggregation operations,Most of the existing methods ensure strong privacy at the expense of very limited aggregation.

We propose a PPSA scheme that encrypts sensitive user data to prevent the disclosure of privacy by both external analysts and aggregation service providers And fully supports online user behavior analysis selective aggregate functions while ensuring differential privacy.

We implemented our method and used a trace-driven evaluation based on a real online behavior dataset to evaluate its performance.

Results of the experiment show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable overheads for computation and communication.

Introduction

Privacy-Preserving Selective Aggregation ONLINE user behavior is a demonstration of an alysis study and why e-commerce platform users and web applications are behaving.It has been widely used in practice, especially in commercial environments,Political campaigns and development of web applications.Data aggregation is one of the most critical behavioral analysis operations.

Currently, user data aggregation tasks are outsourced to third party data aggregators, including Google Analytics, com Score,User data aggregation tasks are currently outsourced to third party data aggregators, including Google Analytics, com Score.