2017/10/16 by Michael Carl Tschantz, Tschantz, Michael Carl, Shayak Sen +3 · 1 citation
Computer Science · Social Sciences · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1710.05899
openalex publication_date 2017/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present associative and causal views of differential privacy. Under the associative view, the possibility of dependencies between data points precludes a simple statement of differential privacy's guarantee as conditioning upon a single changed data point. However, we show that a simple characterization of differential privacy as limiting the effect of a single data point does exist under the causal view, without independence assumptions about data points. We believe this characterization resolves disagreement and confusion in prior work about the consequences of differential privacy. The associative view needing assumptions boils down to the contrapositive of the maxim that correlation doesn't imply causation: differential privacy ensuring a lack of (strong) causation does not imply a lack of (strong) association. Our characterization also opens up the possibility of applying results from statistics, experimental design, and science about causation while studying differential privacy.