private range evaluation

This work addresses the challenge of performing range counting over multi-party distributed geospatial data while simultaneously preserving query privacy, ensuring computational efficiency, and maintaining accuracy in the presence of overlapping data—goals that existing methods struggle to reconcile. To this end, the paper proposes the PPRC protocol, which for the first time achieves a unified optimization of these three objectives. PPRC leverages two key techniques: Private Range Predicates (PRP) and Oblivious Linear Counting (OLC), replacing secure comparison with encrypted membership testing and employing lightweight cryptographic operations to enable efficient and secure range evaluation and aggregation. Experimental results on both real-world and synthetic datasets demonstrate that PPRC reduces estimation error by up to 55× and improves runtime performance by as much as 37× compared to baseline approaches.

研究提出一种自动密码分析工作流程，通过生成、测试和优化假设来发现密码系统的缺陷。方法包括识别代数映射错误及分布差异，已验证八个已发布构造的失败。

To address privacy risks arising from access pattern leakage in encrypted range queries under cloud environments, this paper proposes a novel scheme that jointly achieves strong security guarantees and high efficiency. Methodologically, it introduces, for the first time, a learnable spatial index into encrypted settings, integrating Paillier homomorphic encryption with a hierarchical prediction architecture. It further designs a noise-injected bucket mechanism and a permutation-based secure bucket prediction protocol, augmented by a secure point extraction protocol, to simultaneously protect data confidentiality, query content, and access patterns. Experimental evaluation on both real-world and synthetic datasets demonstrates that the proposed scheme significantly outperforms state-of-the-art approaches in query latency and throughput, while providing rigorous formal security proofs under standard cryptographic assumptions.

Existing searchable encryption schemes struggle to simultaneously support Boolean range queries over spatial data while preserving both access and search pattern privacy. This work proposes BRASP, the first scheme to achieve strong privacy guarantees for Boolean range queries on encrypted spatial data. BRASP constructs an encrypted inverted index using Hilbert curve prefix encoding and, under a dual non-colluding server architecture, integrates index shuffling, ID field redistribution, and a forward-secure mechanism to effectively conceal query patterns while supporting dynamic updates. Experimental evaluation on real-world datasets demonstrates that BRASP achieves low computational and communication overhead alongside high practicality. To ensure reproducibility, the implementation has been made publicly available.

This work addresses the frequent disconnect between the mathematical certainty of numerical values in cryptographic protocols and their concrete representations, which undermines interoperability and formal verification. Drawing from representation theory, the paper introduces three classes of representations—algorithmically approximable, finitely precisely describable, and canonically normalizable—and proves that no universal computable canonicalizer can transform arbitrary approximate programs into a unique finite encoding. It extends the canonical encoding paradigm of the rational number system Σ_Q to practical cryptographic objects. By integrating computability theory with canonical serialization techniques, the approach is applied to symmetric and asymmetric encryption, hashing, and blockchain integrity protocols. Case studies such as Snaproot demonstrate that canonical representations are essential for achieving precise protocol specifications, ensuring interoperability, and enabling byte-level correctness arguments.

Existing fully homomorphic encryption (FHE) schemes struggle to efficiently and accurately support mixed arithmetic and comparison operations within a unified framework, often resorting to costly scheme switching or error-prone polynomial approximations. This work proposes a novel space-switching technique that enables seamless integration of these two operation types in FV-like schemes by leveraging plaintext space reduction from ℤ_{p^r} to ℤ_p, modulus lifting, and digit decomposition. For the first time, this approach achieves error-free, low-overhead hybrid computation within a single homomorphic encryption framework. Experimental results on representative database workloads demonstrate a 17× speedup over conventional scheme-switching methods and a 15× improvement compared to direct comparison approaches, substantially enhancing the practicality of privacy-preserving computation.

Existing formal methods struggle to verify the privacy guarantees of modern differential privacy libraries that employ complex programming patterns such as higher-order functions, local state, and interactive algorithms. This work proposes a probabilistic higher-order separation logic that, for the first time, incorporates first-class support for privacy budgets within separation logic, treating them as composable resources to enable modular reasoning. Implemented in the Rocq proof assistant, the logic successfully verifies differential privacy programs featuring higher-order combinators, caching, and interactive mechanisms. Furthermore, the authors construct a formally verified library of mechanisms, including the online sparse vector technique and privacy filters inspired by OpenDP, thereby enabling end-to-end verification of client programs.

Existing symbolic protocol verification tools struggle to support full operations in Diffie-Hellman groups—particularly exponent addition—limiting precise modeling and verification of related protocols. This work proposes a computationally sound approximation of the complete Diffie-Hellman theory and designs a corresponding semi-decision procedure, enabling, for the first time, mainstream symbolic verification tool Tamarin to handle full group operations including multiplication (i.e., exponent addition), thereby overcoming limitations imposed by the finite variant property. The approach successfully verifies the security of the ElGamal encryption scheme and reproduces a known attack on the MQV protocol, demonstrating its effectiveness and practical utility in real-world protocol analysis.

This work addresses the challenge of efficiently performing privacy-preserving statistical analysis on sensitive data in domains such as healthcare and finance, where existing two-party computation (2PC) protocols suffer from poor performance for statistical functions. The authors propose SafeStats, an efficient 2PC-oriented toolkit for secure statistical analysis, which introduces three key innovations: an equality-test-free shifted frequency counting technique, a piecewise indicator-based counting sort, and a range-reduction protocol integrating binary search. These designs collectively reduce both computational and communication overhead. Experimental evaluation demonstrates that SafeStats achieves superior performance across 14 common statistical tasks; for instance, it accelerates chi-squared tests by 1.5× and reduces communication volume by 4.2× compared to generic 2PC libraries.

This work addresses the problem of efficiently verifying distributional properties under differential privacy constraints with an untrusted prover. We establish the first theoretical framework for distribution property testing under differential privacy, integrating techniques from interactive proofs, local differential privacy, and privacy amplification. The study systematically analyzes the equivalence and complexity differences between private-coin and public-coin protocols across varying privacy parameters. Our main contributions include a lossless transformation from private-coin to public-coin protocols under specific privacy regimes, and the construction of a single-message Merlin–Arthur protocol for product distribution testing that achieves optimal sample complexity.