arXiv:2503.18721v3 Announce Type: replace-cross Abstract: Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information. In this paper, we consider the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) in the context of differential privacy. Given that the limiting distribution of the empirical estimate of dHSIC is a complicated Gaussian chaos, constructing tests in the non-private regime is typically based on permutation and bootstrap methods. To detect joint
The increasing prevalence of AI and data-driven applications necessitates robust methods for privacy-preserving data analysis, especially for sensitive information. This research addresses the persistent challenge of conducting complex statistical tests while adhering to stringent privacy standards like differential privacy.
This work is crucial for enabling the responsible development and deployment of AI and statistical applications that rely on sensitive datasets by ensuring data utility is maintained without compromising individual privacy. It directly impacts the trustworthiness and ethical boundaries of data science.
This paper offers a new, more robust method for performing joint independence tests under differential privacy constraints, advancing the state-of-the-art beyond traditional permutation and bootstrap methods. It provides a more statistically sound approach to identifying dependencies in private data.
- · AI ethics and privacy researchers
- · Healthcare and finance sectors
- · Data scientists working with sensitive information
- · Privacy-preserving AI development
- · Organizations with weak data privacy practices
- · Methods relying on less rigorous privacy definitions
Improved statistical rigor for privacy-preserving data analysis will enhance the reliability of insights derived from sensitive datasets.
Broader adoption of such methods could lead to new collaborations and data sharing initiatives where privacy was previously a prohibitive barrier.
This could contribute to the development of more trustworthy AI systems, fostering greater public acceptance and regulatory clarity around data-driven technologies.
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