Learning from Leakage: Database Reconstruction from Just a Few Multidimensional Range Queries

—Searchable Encryption (SE) has shown a lot of promise towards enabling secure and efficient queries over encrypted data. In order to achieve this efficiency, SE inevitably leaks some information, and a big open question is how dangerous this leakage is. While prior reconstruction attacks have demonstrated effectiveness in one-dimensional settings, extending them to high-dimensional datasets remains challenging. Existing methods either demand excessive query information (e.g. an attacker that has observed all possible responses) or produce low-quality reconstructions in sparse databases. In this work, we present REMIN, a new leakage-abuse attack against SE schemes in multi-dimensional settings, based on access and search pattern leakage from range queries. Our approach leverages unsupervised representation learning to transform query co-occurrence frequencies into geometric signals, allowing the attacker to infer relative spatial relationships between records. This enables accurate and scalable reconstruction of high-dimensional datasets under minimal leakage. We begin with a passive adversary that persistently observes all encrypted queries and responses, and later extend our analysis to an more active attacker capable of poisoning the dataset. Furthermore, we introduce REMIN-P, a practical variant of the attack that incorporates a poisoning strategy. By injecting a small number of auxiliary anchor points REMIN-P significantly improves reconstruction quality, particularly in sparse or boundary regions. We evaluate our attacks extensively on both synthetic and real-world structured datasets. Compared to state-of-the-art reconstruction attacks, our reconstruction attack achieves up to 50% reduction in mean squared error (MSE), all while maintaining fast and scalable runtime. Our poisoning attack can further reduce MSE by an additional 50% on average, depending on the poisoning strategy.