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The solution proposed in [22] is based on Differential Privacy, a recent model of randomization introduced in [41] by Dwork.
As to the two-party techniques, Inan et al. [12] in 2010 presented an approach for PPRL based on differential privacy.
To remedy this problem, we propose an effective correlated time-series data publication solution based on differential privacy by enforcing Series-Indistinguishability and designing a correlated Laplace mechanism.
A method [6] based on differential privacy has been proposed where the algorithm has been factored into two parts: an aggregation/learning phase that can be performed with differential privacy guarantees, and an individual recommendation phase that uses the learned correlations and an individual's data to provide personalized recommendations.
We have seen privacy-preserving methods based on differential privacy applied to real human GWAS data in recent studies (e.g., [ 3- 5]).
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Mathematical models based on differential equations have proved very useful.
For both tasks, we challenged teams to apply data perturbation techniques based on the differential privacy standard.
The solution that we report is based on -differential privacy, and provides a good balance between privacy and data utility.
Roth is a computer science professor who has quite literally written the book on differential privacy (it's titled Algorithmic Foundations of Differential Privacy) and Federighi said Roth described Apple's work on differential privacy as "groundbreaking".
Privacy in big data can be achieved through various means but here the focus is on differential privacy.
We focus here on differential privacy, an approach to privacy that emerged from the area of statistical databases and is now widely applied also in other domains.
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