  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 predict
previously cloaked traits. Second, cloaking digital footprints to avoid one
undesirable inference may degrade the performance of models for other,
desirable inferences (e.g., those driving desired personalized content). In the
light of these research gaps, our contributions are twofold: 1) We propose a
novel cloaking strategy that conceals 'metafeatures' (automatically generated
higher-level categories) and compares its effectiveness against existing
cloaking approaches, and 2) we test the spill-over effects of cloaking one
trait on the accuracy of inferences on other traits. A key finding is that the
effectiveness of cloaking degrades over times, but the rate at which it
degrades is significantly smaller when cloaking metafeatures rather than
individual footprints. In addition, our findings reveal the expected trade-off
between privacy and personalization: Cloaking an undesired trait also partially
conceals other desirable traits.
