This work addresses the challenge of performing expressive, multi-dimensional range queries directly over encrypted data while balancing query efficiency against privacy leakage. Existing searchable encryption and encrypted multi-map (EMM) schemes either reveal access or volume patterns or incur substantial overhead by fully hiding all leakage (e.g. via ORAM) making them unpractical. Building on the static, multi-dimensional EMM framework of Falzon et al., we introduce a family of five EMM variants that provide tunable leakage profiles, spanning from full access- and volume-pattern exposure to near-complete concealment through adaptive padding and dummy-access techniques. The empirical evaluation of our
five EMM variants reveals a clear, quantifiable spectrum of privacy-performance trade-offs. On large-range workloads, the access-hiding schemes offer the best overall balance, with measured average latency slopes of ≈ 0.012 ms/label. For workloads dominated by small result sets, a volume hiding scheme excels, achieving an even lower slope of 0.0032 ms/label by tuning its padding to realistic occupancy bounds. In contrast, fully padded schemes like incur substantially higher overheads, up to two orders of magnitude greater, making them suitable only when maximal leakage resilience is required. These results allow cloud providers with quantitative guidance to deploy encrypted range search that meets both privacy requirements and performance expectations in real-world, multi-attribute database services.

This paper tackles the problem of sample selection bias in machine learning, where the assumption of train and test sets being drawn from the same distribution is often violated. Existing solutions in domain adaptation, such as semi-supervised learning techniques, aim to correct this bias, but their ability to generalize to unseen test sets remains unexplored. To address this issue, specific semi-supervised methods (self-training and co-training) are trained on biased training sets and tested with an unbiased test set drawn from the same distribution. The results of this paper demonstrate that the semi-supervised methods consistently outperformed or matched the baseline models, with self-training exhibiting greater improvement. Through this study, a promising approach is presented to mitigate sample selection bias in machine learning.