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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.
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.
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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Genes were grouped by dynamics using an intersection of results based on differential expression analysis.
Similarly, how should a judge evaluate a physician's testimony based on differential ("clinical") diagnosis?
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.
This document is a primer on differential privacy.
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