Privacy-Aware ML Framework for Dynamic Query Formation in Multi-Dimensional Data
B Bhavani, Haritha Donavalli · International Journal of Advanced Computer Science and Applications · 2025
Interactive data exploration at scale remains constrained by 1) weak adaptability to shifting query workloads, 2) limited and post hoc error guarantees, 3) poor scalability under dynamic, high-dimensional data, 4) sparse user guidance during query formulation, and 5) non-trivial system overheads from learned or probabilistic components. We propose an end-to-end, privacy-aware framework that dynamically forms SQL queries for multi-dimensional data using randomized signals derived from personal web usage. The method integrates: 1) on-device user modeling that converts browsing interactions into preference embeddings under local differential privacy; 2) a constrained-randomization layer that enforces coverage and diversity to avoid filter bubbles while remaining responsive to user intent; 3) a contextual bandit policy (with optional deep reinforcement learning extension) that selects or completes query templates using signals from user profiles, session context, and data synopses; and 4) an error-aware AQP executor combining stratified/pilot sampling, synopsis reuse, and confidence-interval gating with automatic sample escalation. This design directly addresses the above limitations: the bandit adapts online to workload shifts; the AQP layer provides pre-execution feasibility checks and per-query error control; synopsis reuse and AB-tree–style random sampling maintain low latency under updates; and a guidance module (predictive autocompletion with information-gain scoring) reduces user effort while preserving exploration diversity. To evaluate effectiveness, we introduce a privacy-preserving training regimen (federated updates over DP-noised profiles) and a novel benchmark protocol measuring time-to-insight, error compliance under differential privacy, session diversity, and latency against strong baselines. The result is an ML-driven exploration loop that achieves error-bounded interactivity, robust personalization, and scalable performance on evolving, high-dimensional datasets, while providing evaluation metrics that capture both user experience and privacy-preserving guarantees.