Adaptive Target-Charging with Privacy Filters and Individual Accounting

The target-charging technique (TCT), a generalization of the sparse vector technique, monitors whether query answers deviate beyond acceptable bounds while incurring privacy costs only upon significant deviations. We develop a more mature theory of TCT through three contributions. First, we generalize TCT and replace prior simulation-based analyses with a direct stochastic domination argument, yielding a tighter bound and a modular proof structure that affords flexibility in the choice of privacy representation for the base mechanism as well as the composition accountant. Second, we provide an anytime-valid TCT guarantee that integrates with approximate Rényi DP filters. Third, we enable individual-level privacy filters. Together, these provide the first end-to-end TCT construction compatible with privacy filters and individual-level accounting, with practical relevance demonstrated through benchmarks inspired by the impending world-scale deployment of the W3C Attribution standard, which is fundamentally rooted in individual filters.