Researchers devised a way to maintain an AI model's accuracy while ensuring attackers can't extract sensitive information used to train it. The approach is computationally efficient, reducing a longstanding tradeoff between accuracy and privacy.
Researchers devised a way to maintain an AI model's accuracy while ensuring attackers can't extract sensitive information used to train it. The approach is computationally efficient, reducing a longstanding tradeoff between accuracy and privacy.
They also demonstrated that more"stable" algorithms are easier to privatize with their method. A stable algorithm's predictions remain consistent even when its training data are slightly modified. Greater stability helps an algorithm make more accurate predictions on previously unseen data. PAC Privacy automatically estimates the smallest amount of noise one needs to add to an algorithm to achieve a desired level of privacy.
Adding noise can hurt the utility of the results, and it is important to minimize utility loss. Due to computational cost, the original PAC Privacy algorithm was limited to adding isotropic noise, which is added uniformly in all directions. Because the new variant estimates anisotropic noise, which is tailored to specific characteristics of the training data, a user could add less overall noise to achieve the same level of privacy, boosting the accuracy of the privatized algorithm.
Encryption Hacking Computers And Internet Information Technology Computer Modeling Mathematical Modeling Mathematics
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