Privacy-Preserving Analysis of User Interactions in E-Commerce

Problem Statement

E-commerce platforms gather large amounts of user interaction data for
personalized recommendations. However, privacy concerns arise when analyzing
this data. Balancing the need for analysis with user privacy is crucial.

Abstract

This project aims to develop a privacy-preserving system for analyzing user
interactions in e-commerce. Using techniques such as federated learning and
secure multi-party computation, the system will extract valuable insights from
user interactions without compromising individual user privacy.

Outcome

A privacy-preserving analysis system that provides e-commerce platforms with
valuable insights from user interactions while ensuring the anonymity and privacy
of individual users.