Current privacy deployments are only able to estimate heavy-hitter populations (top k) and are unable to satisfy both privacy and utility. Privacy leakage is measured regarding the set difference of at least 1 data owner. Such a model is only able to reduce privacy leakage at the cost of utility.
However, the query is not evaluated or taken into consideration by differential privacy alone. Thus, even a small privacy leakage may completely violate data owners privacy due to a query.
For example, suppose we query everyone at the Brooklyn Bridge to understand how many people are currently at the Brooklyn Bridge. Regardless of the cryptographic technique or privacy mechanism used, the act of responding signals to an adversary that the data owner was indeed at the Brooklyn Bridge.
Rather, K-Privacy's adversary model is in regards to the data owners themselves and the difficulty in linkability. Thus, privacy strength increases as the number of data owners increases. The additional data owners blend with and provide privacy protection.
For example, for location privacy a single data owner will submit simultaneous locations at once. Though using K-Privacy the noise is cancelled out. The adversary must now figure out which location a particular data owner is at, which becomes more difficult as the number of data owners increases.
K-Privacy which is a newly proposed privacy mechanism. Please refer to "K Privacy: Towards improving privacy strength while preserving utility" to appear in Ad Hoc Networks. Volume 80, November 2018, Pages 16-30.
A single data owner will submit multiple locations (or no locations), making it challenging to figure out the exact location. However, utility is preserved due to the multiple round estimation of K-Privacy. We have deployed this as CrowdZen to the UCLA campus.
K-Privacy, a new privacy mechanism that increases in privacy strength while preserving utility.
Calibration of the privacy noise and the number of rounds executed.
Yes, as long as there is a minimum number of data owners participating.
Takes into account the formulated query rather than considering the dataset alone and creating a privacy leakage metric.
Our adversary model is in terms of unlinkability, privacy budget is just 1 characteristic.
We increase the number of data owners, this does not add noise as compared to other models that focus on heavy-hitters only. Increasing the number of data owners is part of our adversary model that provides unlinkability. Other models must alternatively add more noise to achieve stronger privacy.
Research question of increasing privacy strength as the number of data owners increases. Our published results show that the utility remains constant while privacy strength increases.
unlinkability of location traces
