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Replica Analytic's New Estimator Leverages Synthetic Data For A More Accurate Assessment Of Re-Identification Risk In Datasets
"Re-identification risk is the probability that an adversary will correctly match a record in a dataset with a real person and until now
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CIO Applications | Tuesday, August 02, 2022

Replica's estimator can now be utilized with the Replica Synthesis program to evaluate the risks of re-identification in actual datasets more accurately.
FREMONT, CA: "Re-identification risk is the probability that an adversary will correctly match a record in a dataset with a real person and until now, there has been no sufficiently reliable measure of this risk. Access to data and sharing de-identified datasets remain a challenge, in part due to privacy concerns. The re-identification risk estimator we have developed should help data custodians overcome those challenges." says Dr. Khaled El Emam, Senior Vice-President and General Manager of Replica Analytics, the premier science-based synthetic data generation technology provider to the healthcare industry.
Replica Analytics, an Aetion company, has presented a revolutionary strategy to employ synthetic data for a more accurate evaluation of re-identification hazards in datasets, to control privacy risks better, and enable increased data sharing in healthcare and other industries.
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Replica's estimator uses data synthesis technology to provide an unavailable population dataset and produce a significantly more precise calculation of re-identification risks. Since most current estimators lack access to real population data, they cannot compute risk on a population and instead provide a proxy for risk based on strong assumptions. A machine learning model is trained to understand a real dataset's statistical patterns and characteristics to create synthetic data.
The new approach is yet another illustration of the value and potency of SDG technology in identifying and reducing privacy problems and facilitating data exchange. The privacy assurance functionality of the corporation is used to measure any danger in the synthesized data and show that it is significantly lower than the original data if the risk is too high.
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