Differential Privacy

Differential privacy is a formal, mathematical framework within computer science and cybersecurity that enables organizations to perform statistical analysis on large datasets while providing strong, provable guarantees about individual privacy. The core principle is to add a carefully calibrated amount of statistical noise to the results of database queries, ensuring that the output of any analysis remains almost identical whether or not any single individual's data is included in the dataset. This makes it virtually impossible to infer sensitive information about a specific person from the published results, thus protecting against re-identification attacks and allowing for the safe, ethical use of aggregate data for research and service improvement.

1. Foundations of Data Privacy
   1. The Need for Privacy in Data Analysis
      1. Risks of Data Sharing
         1. Identity Disclosure
         2. Attribute Disclosure
         3. Inferential Disclosure
         4. Membership Inference
      2. The Re-identification Problem
         1. Linkage with External Datasets
         2. Quasi-identifiers
         3. Case Studies of Re-identification
         4. Motivations for Adversaries
      3. Privacy Paradox in Big Data
         1. Value of Data vs Privacy Concerns
         2. Collective vs Individual Privacy
   2. Limitations of Traditional Anonymization Techniques
      1. K-Anonymity
         1. Definition and Principles
         2. Equivalence Classes
         3. Generalization and Suppression
         4. Strengths and Weaknesses
         5. Computational Complexity
      2. L-Diversity
         1. Definition and Principles
         2. Addressing Attribute Disclosure
         3. Entropy L-Diversity
         4. Recursive L-Diversity
         5. Limitations
      3. T-Closeness
         1. Definition and Principles
         2. Addressing Distributional Attacks
         3. Earth Mover's Distance
         4. Limitations
      4. Other Syntactic Approaches
         1. P-Sensitive K-Anonymity
         2. M-Invariance
         3. Delta-Presence
   3. Failure of Naive Anonymization
      1. Linkage Attacks
         1. Mechanisms of Linkage
         2. Real-World Examples
         3. Netflix Prize Dataset Attack
         4. AOL Search Data Release
      2. Homogeneity Attacks
         1. Exploiting Lack of Diversity
         2. Sensitive Attribute Inference
      3. Background Knowledge Attacks
         1. Use of Auxiliary Information
         2. Temporal Correlation Attacks
      4. Composition Attacks
         1. Multiple Dataset Releases
         2. Incremental Information Disclosure
   4. Introduction to Differential Privacy
      1. Core Concept of Plausible Deniability
         1. Intuition Behind Plausible Deniability
         2. Individual vs Collective Privacy
      2. DP as a Formal Privacy Guarantee
         1. Mathematical Framing of Privacy
         2. Comparison to Informal Guarantees
         3. Worst-Case Privacy Protection
      3. Shifting Focus from Data to Algorithms
         1. Algorithmic Privacy vs Data Privacy
         2. Implications for Data Analysis
         3. Privacy-Preserving Query Processing