Abstract:
Recently, AI has garnered attention. Privacy, security, and model fairness issues have arisen alongside its advances. Differential privacy, a promising mathematical model, can help solve these problems. For this reason, differential privacy has been widely applied in AI, but no study has documented which differential privacy mechanisms can or have been used to overcome its issues or their properties. This paper demonstrates differential privacy’s capabilities beyond privacy preservation. It can improve security, stabilize learning, build fair models, and impose composition in selected AI areas. This article presents a new perspective on differential privacy techniques for improving AI performance in regular machine learning, distributed machine learning, deep learning, and multi-agent systems.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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