The Independent Component Laplace Process (ICLP) mechanism differs from traditional additive noise mechanisms in several key aspects.
Firstly, the ICLP mechanism treats functional summaries as truly infinite-dimensional objects, allowing for a more accurate representation of complex structured summaries compared to embedding them into finite-dimensional subspaces. This approach overcomes limitations faced by traditional mechanisms that treat each dimension uniformly and struggle with capturing the nuances of infinite-dimensional data.
Secondly, the ICLP mechanism allocates privacy budgets proportionally to the global sensitivity of each component, rather than uniformly across all components like traditional mechanisms. This targeted allocation reduces excess noise injected into "more important" components, enhancing the utility and robustness of sanitized functional summaries significantly.
Lastly, by using regularization empirical risk minimization techniques within the ICLP framework, one can achieve differential privacy while controlling the trade-off between regularization and privacy error. This allows for enhanced utility in sanitized summaries by oversmoothing their non-private counterparts strategically.