Privacy-Preserving Sequential Decision Systems for Regulated Personalization

Regulated personalization systems must decide what information, offer, limit, explanation, or next action to present while preserving privacy and controlling downstream harm. Conventional sequential recommenders optimize engagement over behavior traces, but regulated settings also require privacy budgets, evidence constraints, risk limits, and auditable tool use. This paper proposes Privacy-Preserving Sequential Decision Systems (PPSDS), a synthetic architecture for regulated personalization that combines efficient sequence modeling, differentially private behavior summaries, constrained distributional policy gradients, hybrid evidence retrieval, distributed RAG, and contract-bound agent interoperability. PPSDS extends Ideological Drift Detection in Governed Enterprise Knowledge Bases with a drift sentinel for personalization narratives and extends Risk-Aware Financial RAG with Distributional Retrieval Policies with action-level privacy and tail-risk gates. In a simulated personalization benchmark spanning finance, benefits, and customer-support recommendations, PPSDS preserves most of the utility of an unconstrained sequential policy while reducing simulated privacy exposure by 72.0%, lowering conditional value-at-risk of adverse outcomes by 41.5%, and eliminating contract-violating evidence accesses.