Abstract
While personalization is key to increase the usability of online services, disclosing one's preferences is undesirable from a privacy perspective, because it enables profiling through the linkage of what may otherwise be unlinkable service invocations. This paper considers an easily implementable class of obfuscation strategies as a means to mitigate these risks, and examines its privacy/utility tradeoff. Our results are based on simulations that take place within a modular evaluation framework that can seamlessly accommodate real-world data. We conducted experiments with different simulated behaviors and using two preference populations, namely a population of maximally diverse preferences and one consisting of the movie preferences of some Netflix users. We measure utility in a way that is specific to the application of preference obfuscation. Privacy is measured in terms of unlinkability, with respect to two different adversaries. Our results show that reasonable privacy/utility tradeoffs require the disclosure of only small amounts of preference information.
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