Abstract The proliferation of spatial-textual data applications has created significant challenges in securely managing such data within untrusted cloud environments. Existing encrypted spatial-textual data retrieval schemes primarily focus on static data and overlook the complexities of practical data updates, particularly lacking robustness in managing irrational updates. In this paper, we introduce a novel robust dynamic encrypted spatial-textual data search scheme, called , that enhances existing systems by addressing the challenges of security and robustness in data dynamic settings. This is the first scheme to simultaneously achieve forward security, Type-I $$^-$$ - backward security, and enhanced robustness for boolean range queries on spatial-textual data. We formally define the security model and classify three levels of robustness. Our customized Asymmetric Scalar-Product-Preserving Encryption (ASPE) design incorporates an “update check mechanism” can efficiently mitigate repeated and disruptive update attacks while supporting efficient search and update permission control. Experimental evaluations demonstrate that only maintains 100% precision and recall while showing practical search efficiency, even outperforming the existing static scheme with a similar ASPE-based approach.

Cuckoo hashing serves as a fundamental technique in various privacy-enhancing cryptographic primitives, such as Private Information Retrieval, Symmetric Searchable Encryption, owing to its excellent performance. However, achieving space-efficient Cuckoo hashing that maintains fast insertion and query operations, while facilitating its applications with customized design, remains highly challenging. In this work, we propose a multi-segment permutation-based Cuckoo hashing (MS-PCH) that can be efficiently parallelized on multi-threaded platforms, followed by a strategy for further parallelization over the hashing within single segments (MH-MS-PCH). To demonstrate its practical utility, we then investigate its application on encrypted data search by constructing full-fledged Public Key (Authenticated) Encryption with Keyword Search schemes (PCH(-MD)-PEKS and PCH(-MD)-PAEKS). We evaluate the performance of these schemes on a public dataset, showing that, with 16 segments and 4 hash functions, both PCH(-MD)-PEKS and PCH(-MD)-PAEKS outperforms the plain PEKS and PAEKS across index generation, query, and update. Notably, our optimized Cuckoo hashing achieves up to a 10 times improvement over plain cuckoo hashing, and our enhanced P(A)EKS schemes demonstrate approximately a 6 times improvement in index generation efficiency and a 335 times acceleration in query processing.

Searchable symmetric encryption (SSE) enables keyword search over encrypted data but inevitably leaks patterns that adversaries can exploit to infer sensitive information. Differential privacy (DP) has been adopted to mitigate such leakages efficiently, yet existing DP-enhanced schemes are limited to single-user settings, lacking support for multi-user data sharing under diverse privacy needs. In such settings, distinct encryption keys and heterogeneous privacy levels raise two critical challenges: (i) leakage amplification, a prevalent yet under-explored threat where adversaries infer keywords from low-privacy data and link them to high-privacy data in cross-user retrieval; and (ii) inefficient privilege management, where cross-user search requires cumbersome key transmissions and frequent updates due to changing privileges. To bridge these challenges, we propose DPE-MUSE, a DP-Enhanced Multi-User Searchable Encryption scheme that achieves secure mutual retrieval while hiding access and size patterns. At its core is an adaptive keyword-level budget allocation strategy that derives budgets from data distributions and mitigates cross-user inference. A unified key management strategy further eliminates direct key sharing and streamlines privilege updates. Experiments on the Enron dataset show that DPE-MUSE achieves high recall (98.96%) under strict privacy requirements with relative low overhead, requiring only 58.951 ms for budget computation on a dataset of size 214.

Abstract Sharing electronic protected health information (ePHI) is highly beneficial in public affairs. Constructing a cloud-assisted ePHI retrieval service represents a modern strategy that enhances cost-effectiveness and efficiency, making it a promising solution. However, widespread implementation is hindered by concerns over privacy violations. Symmetric Searchable Encryption (SSE) has emerged as a practical approach for ensuring data privacy and efficient retrieval in the cloud without decryption. Nonetheless, existing SSE schemes encounter challenges, including secure and efficient data updates, effective search-update permission control, and cross-platform adaptability. To address these limitations, we propose a novel variant of Dynamic Searchable Symmetric Encryption known as SEAC, tailored for secure, cloud-assisted sharing of ePHI. SEAC is formalized within a multi-client/single-server model, enabling authorized clients to perform search or update on encrypted data. The concrete construction integrates dynamic cryptographic accumulators with an efficient binding mechanism for index structures, offering two key features: (1) sublinear conjunctive keyword search that ensures forward and Type-II backward privacy, and (2) lightweight, updatable permission control that effectively mitigates read-write confusion while supporting scalable user revocation and re-authorization. We developed both a localized and a web-based implementation, demonstrating its efficiency and scalability in practical applications.

Graph databases, which essentially store network nodes and edge relationships between them, offer a promising solution for managing the large and dynamic Internet of Things (IoT) network. However, as data grows explosively, end devices cannot carry it, forcing organizations to outsource storage to cloud servers, bringing privacy risks, such as data leakage. Existing privacy-preserving graph search schemes either fail to support secure and efficient multigranularity updates over encrypted complicated property graph or neglect multiuser access control, greatly limiting their practicability. In this article, we propose a novel dynamic encrypted property graph search system along with three full-fledged constructions, named Spidey. We model the property graph and introduce two well-designed structures: bidirectional index and delete list, which form the foundation of our schemes. The basic schemeDGraphsupports efficient, fine-grained sublinear queries and updates with the complexity of both attribute-grained update and node-grained deletion being$\mathcal {O}(1)$, while ensuring both forward privacy (FP) and backward privacy (BP). Two enhanced schemes$\mathtt {DGraph\_RW}$and$\mathtt {DGraph\_Role}$further incorporate lightweight operation-based and (hierarchical) role-based access control, respectively, while avoiding encrypted index expansion and minimizing the impact on search efficiency. Both theoretical comparison and experiment results demonstrate their usability and scalability. Notably, for attribute-grained update,DGraphis$2.5\times $faster than ODXT (by Patranabis and Mukhopadhyay), and for node-grained deletion, with each node associated with 12 attributes,DGraphis$30\times $faster than ODXT.

This paper investigates high-performance implementations of Falcon scheme, which is one of the four post-quantum cryptography algorithms standardized by NIST. We explore high-throughput implementation solutions by improving how critical components of Falcon execute on the GPU, with a focus on low latency requirements. Our research reveals that some components of Falcon cannot fully exploit the parallelism of the GPU. Consequently, we propose a heterogeneous parallel implementation of the Falcon signature scheme that utilizes the parallelism of both CPUs and GPUs, while mitigating the additional overhead introduced by heterogeneous computing. Finally, we conduct evaluations on three typical testbeds. The experimental results show that our improved GPU implementation is 222 percent faster for signing and 13.9 percent faster for verification in cloud scenarios compared to the state-of-the-art GPU implementations. On an embedded GPU platform (Jetson AGX Orin), our heterogeneous parallel implementation outperforms the CPU multi-threaded implementation by 46 percent and the GPU implementation by 297 percent.

Searchable symmetric encryption (SSE) schemes allow a client to store encrypted data with a storage provider and retrieve corresponding documents without revealing the content or search keywords to the provider. However, achieving efficient SSE schemes often comes at the cost of statistical information leakage, including search, access and size patterns. The known solutions from fully homomorphic encryption or oblivious RAM often admit poor performances due to significant computational and communication overheads. Additionally, the demand for rich search expressiveness, such as Boolean queries, further complicates the design. In this paper, we introduce NEMO, a novel SSE achieving a good balance between efficiency, security and query expressiveness. NEMO utilizes function secret sharing (FSS) and replicated secret sharing-based multi-party computation (MPC) protocol, but is highly optimized for large database. For functionality, NEMO supports arbitrary Boolean queries and enables dynamic updates in a multi-user setting. For security, NEMO achieves minimal leakage by eliminating all search, access, and size patterns, while only allowing the leakage of Boolean formulas in queries. Regarding efficiency, we propose a new FSS for multi-point functions, effectively batching multiple distributed point functions, and an infix-to-postfix conversion algorithm for Boolean formula to reduce the communication rounds in the MPC protocol. A proof-of-concept implementation of NEMO demonstrates its efficiency, with a search latency of approximately 622 ms for a conjunction query with 8 keywords, even with a dataset exceeding 1 million documents.

The immutability of blockchains is critical for cryptocurrencies, but an imperative need arises for the redaction of on-chain data due to privacy-protecting laws like GPDR. Recently, Ateniese et al. (EuroS&P 2017) proposed an elegant solution to this problem based on chameleon hash functions, followed by many subsequent works. While these works offered a solution to the permissioned blockchain, the approaches were not efficient enough for the permissionless setting, in terms of either security (which may cause inconsistent historical transactions) or performance (only up to a few hundred nodes). In this paper, we investigate this problem and present Wolverine, a redactable permissionless blockchain. First, we present a formal redactable blockchain model, carefully considering transaction consistency. Next, towards a practical scheme, we introduce the novel concept of non-interactive chameleon hash (NITCH). NITCHs dynamically distribute a trapdoor key among a group and each party in the group can compute its partial share without communicating with others. Anyone who possesses enough shares can then find a valid hash collision. To prevent the static group from being compromised after a sufficiently long time, we provide a generic transform from NITCHs to decentralized random beacons (DRBs) and design a committee evolution protocol based on DRBs that refresh the group after every fixed interval of time. Based on NITCH and the committee evolution protocol, we construct Wolverine which offers important features such as scalability, transaction consistency, and public accountability. Finally, we demonstrate the practicality of Wolverine by giving a proof-of-concept implementation based on Bitcoin in Golang.

Snow cover plays a significant part in global surface radiation budget, hydrologic cycle and climate change. High computational efficiency and consistent accuracy is required to estimate fractional snow cover (FSC) using Landsat-8 OLI data, especially in variable terrain. This study proposes an optimized regression method to estimate FSC in various scenes. This method adaptively adjusts the function parameters according to the relationship of normalized difference snow index (NDSI) and universal ratio snow index (URSI), rather than changing fixed parameters. The main results show that our algorithm yields lower RMSE (13%∼19%) and higher correlation (0.70∼0.90) than USGS FSC product. Evaluation in other regions obtains similar results, indicating that this method is feasible to estimate FSC in a larger region. Therefore, it is a potential approach to generate FSC product.

Searchable Symmetric Encryption (SSE) is a key tool for secure data processing. To date, most of the SSEs were studied alone, while an SSE supporting update operations over encrypted data remained a challenging problem due to various statistical attacks and multi-user environments. In this article, we proposeSeUpdate, the first SSE scheme that simultaneously achieves keyword search and controlled update over encrypted data, with flexible read (search) and write (update) access control policies among multiple users. InSeUpdate, users do not need to share secret keys and a single query enables one to efficiently search all his authorized data. We formally define a security model, and prove our scheme have both forward and backward security. We note that the write permission of an SSE is realized for the first time. We further extend the basic scheme with dynamic access policy update and support of a large number of files. We also implementSeUpdateand some related work. The theoretical and experimental analyses demonstrate our scheme and its extension are practical and efficient.

Metadata plays an essential role in facilitating data organizing, finding, and understanding, but it also contains lots of sensitive information about the data and the data users, e.g., the location where a picture was taken. In practice, when sensitive data is encrypted before being uploaded to untrusted public clouds, an oblivious dilemma comes: if the metadata is totally encrypted, and its functionalities no longer exist; otherwise, sensitive information may be leaked. Hence, a secure and flexible mechanism for processing different fields (marked private or public for different scenario needs) of metadata simultaneously is desirable. We searched the literature for methods of achieving such a goal, it turned out that this was not explicitly considered or reasonably solved before. Therefore, in this paper, we investigate the problem of constructing privacy-enhancing metadata, namely, 1) flexible and tamper-resistant metadata setting, 2) owner-enabled secure search authorization with explicit metadata. Based on the concept of public key encryption with keyword search (PEKS), we propose a novel Authorized Keyword Search over Encrypted Data with Metadata scheme (MD-AKS), which firstly well addressed the above demands. We formalize the security model and prove the security of MD-AKS scheme. Our work maximizes the flexibility of metadata setting in two aspects: the associated metadata can be set as an arbitrary string and the costs of clients are independent of explicit metadata’s complexity. We implement MD-AKS, the theoretical comparison and experiment results further demonstrate the usability and scalability.