Joint Optimization of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

TL;DR

This paper presents a dynamic, cost-effective framework for balancing user privacy and targeted advertising in mobile ecosystems, using online control algorithms without needing knowledge of user behavior changes.

Contribution

It introduces a novel online optimization algorithm that balances privacy, cost, and ad targeting without prior knowledge of user behavior dynamics.

Findings

The framework effectively preserves user privacy while maintaining ad targeting quality.

The proposed algorithm outperforms existing privacy-preserving methods in experiments.

Implementation on a proof-of-concept system demonstrates practical applicability.

Abstract

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers' personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users' privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework's applicability by implementing its critical components using POC (Proof Of Concept) `System App'. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.