English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We study privacy-utility trade-offs where users share privacy-correlateduseful information with a service provider to obtain some utility. The serviceprovider is adversarial in the sense that it can infer the users' privateinformation based on the shared useful information. To minimize the privacyleakage while maintaining a desired level of utility, the users carefullyperturb the useful information via a probabilistic privacy mapping beforesharing it. We focus on the setting in which the adversary attempting aninference attack on the users' privacy has potentially biased information aboutthe statistical correlation between the private and useful variables. Thisinformation asymmetry between the users and the limited adversary leads tobetter privacy guarantees than the case of the omniscient adversary under thesame utility requirement. We first identify assumptions on the adversary'sinformation so that the inference costs are well-defined and finite. Then, wecharacterize the impact of the information asymmetry and show that it increasesthe inference costs for the adversary. We further formulate the design of theprivacy mapping against a limited adversary using a difference of convexfunctions program and solve it via the concave-convex procedure. When theadversary's information is not precisely available, we adopt a Bayesian viewand represent the adversary's information by a probability distribution. Inthis case, the expected cost for the adversary does not admit a closed-formexpression, and we establish and maximize a lower bound of the expected cost.We provide a numerical example regarding a census data set to illustrate thetheoretical results.

Privacy-Utility Trade-Offs Against Limited Adversaries