AAPPeC: Agent-based Architecture for Privacy Payoff in eCommerce

TL;DR

This paper proposes an agent-based architecture that enables consumers to receive financial rewards for sharing personal data with online businesses, addressing privacy concerns in eCommerce.

Contribution

It introduces a multi-agent system that evaluates, negotiates, and manages privacy payoffs, providing a novel framework for consumer compensation in data sharing.

Findings

Agents effectively categorize and value personal data.

The system successfully negotiates privacy payoffs based on trustworthiness.

Consumers receive quantifiable benefits for sharing information.

Abstract

With the rapid development of applications in open distributed environments such as eCommerce, privacy of information is becoming a critical issue. Today, many online companies are gathering information and have assembled sophisticated databases that know a great deal about many people, generally without the knowledge of those people. Such information changes hands or ownership as a normal part of eCommerce transactions, or through strategic decisions that often includes the sale of users' information to other firms. The key commercial value of users' personal information derives from the ability of firms to identify consumers and charge them personalized prices for goods and services they have previously used or may wish to use in the future. A look at present-day practices reveals that consumers' profile data is now considered as one of the most valuable assets owned by online businesses. In this thesis, we argue the following: if consumers' private data is such a valuable asset, should they not be entitled to commercially benefit from their asset as well? The scope of this thesis is on developing architecture for privacy payoff as a means of rewarding consumers for sharing their personal information with online businesses. The architecture is a multi-agent system in which several agents employ various requirements for personal information valuation and interaction capabilities that most users cannot do on their own. The agents in the system bear the responsibility of working on behalf of consumers to categorize their personal data objects, report to consumers on online businesses' trustworthiness and reputation, determine the value of their compensation using risk-based financial models, and, finally, negotiate for a payoff value in return for the dissemination of users' information.