Context-Aware Privacy Parameter Orchestration for Multi-Domain Analytics

Current systems like Liu et al.'s (2024) synthetic data framework require manual privacy-utility trade-offs, while Joshi et al. (2024) note that fixed parameters lead to suboptimal outcomes across domains. This research introduces a cross-domain orchestration layer using reinforcement learning to dynamically set privacy parameters. For example, healthcare analytics (per Orthi et al., 2025) would prioritize stricter privacy than marketing analytics (per Sinha, 2024). The controller would ingest metadata (e.g., data type, regulatory constraints) and feedback from utility metrics to optimize parameters in real time. This diverges from static approaches in Aziz et al. (2023) by treating privacy as a context-dependent variable rather than a fixed budget. The impact includes automated compliance and maximized utility without sacrificing privacy.

References:

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