The Impact of Cloaking Digital Footprints on User Privacy and Personalization
Sofie Goethals, Sandra Matz, Foster Provost, Yanou Ramon, David, Martens

TL;DR
This paper investigates the long-term effectiveness of cloaking digital footprints to protect user privacy, proposing a novel strategy that conceals higher-level features and examining its impact on personalization and inference accuracy.
Contribution
It introduces a new cloaking method targeting metafeatures and evaluates its performance over time and its spill-over effects on other inferences.
Findings
Cloaking effectiveness diminishes over time.
Cloaking metafeatures reduces the rate of effectiveness degradation.
Cloaking one trait partially conceals other desirable traits.
Abstract
Our online lives generate a wealth of behavioral records -'digital footprints'- which are stored and leveraged by technology platforms. This data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). Prior work has proposed a potential remedy: The cloaking of users' footprints. That is, platforms could allow users to hide portions of their digital footprints from predictive algorithms to avoid undesired inferences. While such an approach has been shown to offer privacy protection in the moment, there are two open questions. First, it remains unclear how well cloaking performs over time. As people constantly leave new digital footprints, the algorithm might regain the ability to…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Privacy-Preserving Technologies in Data · Hate Speech and Cyberbullying Detection
