Stochastic Privacy
  Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to enhance the quality of service via personalization of content and to maximize revenues via better targeting of advertisements and deeper engagement of users on sites. To date, service providers have largely followed the approach of either requiring or requesting consent for opting-in to share their data. Users may be willing to share private information in return for better quality of service or for incentives, or in return for assurances about the nature and extend of the logging of data. We introduce \emph{stochastic privacy}, a new approach to privacy centering on a simple concept: A guarantee is provided to users about the upper-bound on the probability that their personal data will be used. Such a probability, which we refer to as \emph{privacy risk}, can be assessed by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on allowable or communicated privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data. 
Adish Singla, Eric Horvitz, Ece Kamar, Ryen White
cs.AI