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The Role of Differential Privacy in Protecting Sensitive Information in the Era of Artificial Intelligence
- Differential privacy (DP) protects data by adding noise to queries, preventing re-identification while maintaining utility, addressing Artificial Intelligence-era privacy challenges.
- In the era of Artificial Intelligence, traditional anonymization techniques have proven inadequate against sophisticated re-identification attacks, leading to the importance of robust mechanisms like DP.
- DP introduces mathematically guaranteed noise to dataset queries while maintaining statistical utility, used in healthcare, finance, and government data analytics.
- Cynthia Dwork's work on DP introduced the concept, its mathematical basis, and its application in privacy-preserving data analytics.
- Recent research has focused on applying DP in various domains, such as Google’s RAPPOR and integration with census data collection to ensure confidentiality.
- Key concepts of DP include its definition, mathematical foundation using the (ε, δ)-differential privacy model, and noise addition mechanisms like Laplace and Gaussian mechanisms.
- DP has applications in Healthcare AI by protecting patient privacy in EHRs and in Finance AI for fraud detection and risk assessment.
- Challenges of DP include determining an optimal privacy budget (ε) and managing data utility, with research focusing on adaptive noise mechanisms and federated learning.
- Author Arfi Siddik Mollashaik, a Solution Architect at Securiti.ai, specializes in data security, privacy, and compliance, with a focus on enhancing data protection programs.
- Future advancements in privacy-preserving machine learning and adaptive DP models are expected to enhance big data analytics' security and effectiveness.
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