Reliable location selection and hierarchical interleaved bloom filter based iris template protection

Due to the fact that most existing digital watermarking schemes can only add watermark information once, repeated additions will overwrite the original watermark information, making it impossible to achieve multi-level traceability of data. In response to the above issues, this paper proposes a multi-level database image watermark embedding scheme (MIWC) based on the Chinese remainder theorem. Taking image watermarks as carriers, MIWC preprocesses images using methods such as Haar wavelet transform and Bloom filter to form a pixel. It then performs secret segmentation on watermark information in accordance with the properties of the Chinese Remainder Theorem, enabling hierarchical and item-by-item addition of the watermark information. Furthermore, it records and traces the entire data flow chain. Functional analysis demonstrates that MIWC possesses the multi-level watermark embedding capability that existing schemes lack, thus holding high practical application value. Experimental results indicate that MIWC is reversible, with both watermark embedding and extraction exhibiting high efficiency. Even with the embedding of multi-level watermarks, MIWC remains highly efficient and lightweight. Meanwhile, MIWC also demonstrates strong robustness, being capable of resisting geometric attacks (including rotation and scaling) and common attacks (including JPEG compression, JPEG2000, Gaussian white noise, and salt-and-pepper noise) targeting the watermark.

MotivationA central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times.ResultsWe present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool.

MotivationIdentifying which documents in a large database contain a query string is a fundamental problem in Information Retrieval and Computational Biology. We focus on the approximate version of this problem for genomic sequences: the result set may contain false positive matches but no false negatives. State-of-the-art solutions rely on Bloom filters to index all k-mers (substrings of fixed length k) in the documents. To answer a query, documents sharing at least a user-prescribed fraction of query k-mers (typically 75%–80%) are returned.ResultsHere, we explore an alternative index design based on k-mer minimizers and integer compression methods. We show that a careful implementation of this design outperforms previous solutions based on Bloom filters by a wide margin: the index has lower memory footprint and faster query times, while false positive matches have only a minor impact on the ranking of the documents reported. This trend is robust across genomic datasets of different complexity and query workloads.Availability and implementationThe software is freely available at github.com/yhhshb/kaminari under the MIT license. Reproducibility scripts are available at github.com/vicLeva/benchmarks_kaminari.

K-mer counts are fundamental in many genomic data analysis tasks, providing valuable information for genome assembly, error correction, and variant detection. State-of-the-art k-mer counting tools employ various techniques, such as parallelism, probabilistic data structures, and disk utilization, to efficiently extract k-mer frequencies from large datasets. The distribution of k-mer counts in raw sequencing reads reveals key genomic characteristics such as genome size, heterozygosity, and basecalling quality. The number of reads containing a k-mer has also shown application in genome assembly and sequence analysis. We present ntStat, a toolkit that employs succinct Bloom filter data structures to track both k-mer count and depth information and use in downstream applications. ntStat models the k-mer count histogram using evolutionary computation, and infers valuable insights about the genome, sequencing data, and individual k-mers, de novo. ntStat consistently ran faster than DSK, BFCounter, hackgap, and Squeakr in all of our tests. Jellyfish performed faster than ntStat for human data with k = 25 but fell behind with k = 64. KMC3 was faster overall but at a high disk usage and memory cost. ntStat also used less memory than other non-disk-based k-mer counters and typically, 99.5-99.9% of the k-mers processed by ntStat are counted correctly. ntStat’s histogram analysis module detected heterozygosity percentages and k-mer coverage for long-read datasets simulated from a diploid human genome with less than 1% and 0.5-fold difference to the ground truth. The analysis of simulated long read datasets showed an average error of just 2% in k-mer robustness estimates.

Abstract In this paper, a searchable encryption scheme for cloud data is proposed to address the limitations of existing schemes, which suffer from inefficient index construction and search process, as well as a lack of support for dynamic updates or the complexity and inefficiency of update operations. Firstly, the efficiency and flexibility of index construction are improved by designing a reverse index based on B+ trees, coupled with Bloom filters. Vector matching ideas are employed to enhance the efficiency of searching encrypted cloud data. Secondly, leveraging the bitmap index, a combination design of forward and reverse indices is utilized to improve update efficiency, concealing update types to protect access patterns. Finally, the security analysis and comparative experimental results collectively demonstrate the feasibility, efficiency, and security of the proposed scheme, verifying its potential in practical applications.

Replica node attacks, a common issue in wireless sensor networks (WSNs), can cause major damage. Most traditional replica node detection protocols are designed for static WSNs and are limited by easy information leakage, high storage and communication overhead, and a short network lifetime. Therefore, this study proposes a multi-point collaborative mobile replica node detection protocol based on key negotiation, referred to as KN-MCDP. The KN-MCDP scheme is designed for use in mobile wireless sensor networks (MWSNs) where a limited number of mobile nodes are deployed in static WSNs. The protocol can not only identify replica nodes in static WSNs but also determine whether a mobile node is a replica, thereby providing enhanced network protection. When cluster head nodes and mobile nodes communicate, they encrypt the exchanged information using digital signature technology, ternary symmetric polynomial technology, and symmetric encryption technology, thus preventing information leakage collected by cluster head nodes. Collecting network information using Bloom filters on cluster head nodes and mobile nodes reduces network storage and communication overhead. In different phases, the protocol employs cluster head nodes, mobile nodes, and the base station to identify and isolate replica nodes. This approach balances the energy overhead of the network and extends its lifetime. The experimental results demonstrate that the KN-MCDP protocol can achieve a high detection rate, reduce the network’s storage and communication overhead, balance energy overhead, and extend the network lifetime.

Hot data identification plays a critical role in a wide range of computing systems, including memory hierarchy management, database optimization, and large-scale storage infrastructures. Its importance has further increased with the emergence of non-volatile memory (NVM) technologies. However, many existing studies have not fully succeeded in accurately identifying hot and cold data due to excessive computational overhead, large memory requirements, and limited accuracy, primarily stemming from ineffective data structures for recording both recency and frequency information. To date, two representative data structures-bit-array counters and multiple bloom filters-have been widely adopted. While bit-array counters effectively capture access count (i.e., frequency) information, they fail to consider recency. Multiple bloom filters were proposed to record both frequency and recency information and have therefore been widely employed. Nevertheless, many hot data identification schemes based on multiple bloom filters still suffer from low accuracy due to fundamental limitations of the underlying data structure. To overcome these inherent limitations, this paper proposes Tierra, a novel hot data identification scheme based on a completely new data structure employing asymmetric multilevel arrays. These asymmetric arrays improve performance by significantly reducing internal data movement by 3.1×. In addition, Tierra incorporates a recency-aware request screening mechanism based on an enhanced stack distance approximation algorithm, which substantially reduces computational overhead while improving identification accuracy. Comprehensive evaluations using diverse real-world workloads demonstrate that the proposed Tierra achieves high accuracy, with an average true identification rate of 99.4%.

Pattern matching is a key technique in the physical verification of integrated circuit designs and is widely used in lithographic hotspot detection. Existing pattern-matching algorithms face challenges in effectiveness and robustness, especially when processing complex patterns. We propose a pattern-matching algorithm based on augmented vertex hashing that correctly handles polygons with holes and supports fuzzy matching. For exact matching, our method encodes Manhattan polygons into fixed-length hash values using the augmented vertex representation, enabling fast polygon comparisons. For fuzzy matching, we formulate the problem as a constellation problem over augmented vertices and solve it efficiently using a cache-friendly algorithm based on a Bloom filter. Experiments show that our approach is more than 5× faster than the current state of the art on average.

Large language models (LLMs) with the Transformer architecture as the core have made significant progress in the field of natural language processing, and their application value in English teaching has also attracted much attention. In tasks such as text generation, question-answering systems, and translation, the processing capabilities of LLMs have significantly improved. However, existing LLMs have problems such as insufficient coverage of professional knowledge, rough semantic parsing, and weak personalized services. To address the aforementioned issues, this study proposes a dual-path retrieval-enhanced generation scheme that integrates vector databases and intelligent agents, aiming to improve the application of large models in English language teaching. Semantic retrieval of unstructured data in English teaching is realized through vector databases, knowledge is dynamically acquired by combining agents, and the accuracy is improved by using Bloom filters to fuse dual-path retrieval. At the same time, the retrieval efficiency is optimized by an importance-oriented algorithm, and user profiles are constructed based on multi-dimensional data to achieve personalized adaptation. Experiments show that the maximum optimization of the retrieval time of this scheme can reach 26.32%, and the highest retrieval accuracy can reach 86%. The key indicators and scores in tasks such as English knowledge retrieval and question-answering reasoning are better than those of the comparative schemes, providing an effective technical path for intelligent English teaching.

Given a set \(S \subseteq \mathcal {U}\) and a function \(f:S\rightarrow \lbrace 0,1\rbrace ^r\) , a static retrieval data structure for f supports queries that return \(f(x)\) for \(x \in S\) and an arbitrary value from \(\lbrace 0,1\rbrace ^r\) for \(x \in \mathcal {U}\setminus S\) . Retrieval data structures can be used to implement a static approximate membership query (AMQ) data structure, i.e., a Bloom filter alternative, with false positive rate \(2^{-r}\) . The information-theoretic space lower bound for both tasks is \(r|S|\) bits, and here we aim to use space \(r|S|(1+\varepsilon)\) bits for a small overhead \(\varepsilon\) , including succinct constructions with \(\varepsilon = o(1)\) . A well-known approach to this task associates each key \(x \in S\) with a row vector \(\smash{\vec{h}}(x) \in \lbrace 0,1\rbrace ^{m}\) and stores a matrix \(Z\in \lbrace 0,1\rbrace ^{m\times r}\) such that \(\smash{\vec{h}}(x)\cdot Z = f(x)\) for every \(x \in S\) . We propose a new variant where \(\smash{\vec{h}}(x)\) contains a short block of random bits at a random position \(s(x)\) , and is otherwise zero. Sorting the row vectors by \(s(x)\) gives a matrix \(A \in \lbrace 0,1\rbrace ^{n \times m}\) with non-zero entries concentrated in a “ribbon” along a generalized diagonal. This makes a variant of Gaussian elimination particularly efficient at computing Z . We thus obtain simple data structures called Standard Ribbon Retrieval and Homogeneous Ribbon Filter . We then refine the construction using bumping (a variant of backyarding) and overloading (using \(m \lt n\) ) to obtain bumped ribbon retrieval (“BuRR”), with overhead \(\mathcal {O}\!(\frac{\log w}{rw^2})\) , query time \(\mathcal {O}\!(1+\frac{rw}{\log n})\) , and expected construction time \(\mathcal {O}\!\left(nw\right)\) , for a tuning parameter \(w=\mathcal {O}\!\left(\log n\right)\) that opens a trade-off between space and running time. Our experiments reveal our implementations to be the first to simultaneously achieve small overheads and fast running times in practice, with BuRR achieving overheads well below 1 % while being faster than most competitors, which have larger space overheads. This efficiency, including favorable constants, stems from a combination of simplicity, word parallelism, and high locality. We offer a unified theoretical perspective on these three ribbon-based data structures, including a nontrivial rigorous analysis of their running times and memory consumption.

Abstract Keyword search, which identifies transactions associated with specified keywords across historical blocks, is a critical query type in blockchain analytics. However, existing approaches, such as on-chain indexing and off-chain synchronization, may lead to significant space overhead or challenges in maintaining data freshness. To address these challenges, we propose BlockSketch, a novel probabilistic data structure (PDS) that adopts a differentiated encoding strategy, aimed at resolving the trade-off between query performance and storage overhead in blockchain indexing. BlockSketch features a hierarchical filtering architecture that combines Bloom filters and Sketches within a binary tree framework, enabling dynamic structural maintenance. Keywords are categorized as “hot” or “cold” based on their on-chain frequency and encoded into the most suitable component to achieve resource-efficient storage and accurate querying. In addition, BlockSketch integrates two distinct query rules, namely “level-down” and “jump,” to balance query accuracy and efficiency when processing keywords with varying frequencies. Furthermore, we enhance the query efficiency of BlockSketch by merging inefficient lower-level nodes into more compact ones and pruning redundant node checks during query execution. Extensive experiments on a real-world dataset demonstrate that BlockSketch delivers up to 73% faster query processing, achieves 44.56% of the average false positive rate of baselines at low multiplicity and as low as 1.52% at high multiplicity, and saves 79% in storage compared to state-of-the-art methods.

To address the limitations of lightweight medical blockchains in terms of data query efficiency and nonexistence proofs, we propose a novel lightweight medical blockchain data query scheme. First, the XGBoost algorithm is employed to predict medical data weights, with high-weight data storage near the blockchain's root nodes being prioritized, thereby optimizing the storage architecture and enhancing query efficiency. Second, an efficient query method that combines aggregated Bloom filters and Merkle-Huffman (MH) trees is designed. Through segmented filtering and weight optimization, the query path length is reduced, improving the on-chain data query performance. Finally, to address the challenge of the data nonexistence proof, we propose a multi-node collaborative verification mechanism that integrates Bloom filters with a dynamic reputation system. By adaptively selecting high-credibility nodes and employing multi-node consensus, false positives are minimized, ensuring query accuracy and reliability. Theoretical analysis and simulation results show that, compared with existing schemes, the proposed approach improves the query efficiency by approximately 15%. Moreover, integrating multi-node collaborative verification with a dynamic reputation mechanism effectively mitigates malicious attack risk and enhances system security, making it particularly suitable for resource-constrained scenarios such as mobile health care.

Road-safety data in Vehicular Ad Hoc Networks (VANETs) is becoming increasingly complex and diverse which leads to major challenges such as security flaws, ineffective data transmission, and risk of single-point failures. To address these issues, the Information-centric Vehicular Cloud (IVC) network has been adopted to secure data transactions. However, existing IV-based approaches still suffer from high latency and low Packet Delivery Ratio (PDR), which negatively impact the system performance. To overcome these limitations, the research proposed an efficient and secure data validation technique for VANETs. Vehicles generate various types of road safety data, which are transmitted to the destination via unicast forwarding. Caching vehicles are used to store frequently requested data to reduce latency and improve access speed. Also, a probabilistic data verification strategy is implemented, where vehicles verify transient data packets with a certain probability and exchange verification output to enhance accuracy. Each content provider signs the data and attaches verification metadata before transmission, which enables intermediate nodes to validate the content in transit. At the network edge, a Randomized Independent Verification Protocol (RIVP) with a Bloom filter is proposed to rapidly verify content authenticity, even when the complete verification information is embedded in the data itself. Experimental output illustrates that the proposed technique achieves a verification accuracy of 98.95% and a low verification overhead of 1.629%, which outperforms the existing method in terms of both security and efficiency.

Rare disease research relies heavily on secondary use of health data due to the scarcity of clinical guidelines and data sharing between research institutions and hospitals. Linking rare disease patients is challenging due to increased re-identification risk in small cohorts, thus limiting the data's potential for research. Privacy-Preserving Record Linkage (PPRL) enables the linkage of disparate datasets while safeguarding the identities of involved participants. The aim of the present paper is to provide an up-to-date description of the concept and the technical details of the European Patient Identity (EUPID) Services, a configurable PPRL solution which is currently used for rare disease research in Europe to bridge healthcare and research. They support different algorithms for record linkage (configurable selection of quasi-identifiers, various hashing algorithms, phonetic hashing, Bloom filters), re-identification and flexible specification of the pseudonym format. Furthermore, their setup is also flexible whether to install standalone instances or integrate with a central EUPID Services deployment. The EUPID Services have been used in various research applications since 2014. As of July 2025, 6,356 unique patients have been registered to the central EUPID Services within the domain Paediatric Oncology in Europe, and 10,340 pseudonyms for 12 EUPID Contexts have been generated. Within the Austrian Health Data Donation Space, which represents a federated PPRL infrastructure supporting asynchronous record linkage, more than 16 million patients were pseudonymised in six different contexts. Overall, four cases of false negative matches have been identified, which were caused by typing errors. So far, no false positive match has ever been detected. In view of the upcoming European legislatives like the European Health Data Space (EHDS), connecting patient data securely and safely will become increasingly important and useful. The EUPID Services support such linkage in a Findable, Accessible, Interoperable and Reusable (FAIR) manner and thus could represent a vital and proven part of future national and European research networks.

Industrial Internet of Things (IIoT) systems in sectors like manufacturing, energy, and healthcare are increasingly deployed in cloud-assisted operational environments, where network telemetry and security analytics are routinely processed in the cloud. However, these systems remain highly vulnerable to cyber threats from shared threat actors. Among these, low-rate Denial of Service (LDoS) attacks, marked by subtle periodic traffic patterns, are particularly challenging to detect when analyzed in isolation. Cross-organization collaborative detection across cloud platforms can improve identification accuracy, but sharing threat intelligence risks exposing sensitive operational information. To tackle this challenge, we propose Labelled-Threshold Private Set Intersection (LT-PSI), a cryptographic framework that allows two organizational clouds to securely identify common elements whose associated label vectors satisfy a similarity threshold, without revealing any additional data. Our LT-PSI protocol introduces an innovative combination of position encoding, Diffie-Hellman Oblivious Pseudorandom Functions (DH-OPRF), and Bloom filters, effectively transforming threshold-based label similarity matching into efficient and privacy-preserving set membership tests. Particularly, our protocol achieves sublinear online complexity and is well-suited for cloud execution, integrating an adaptive early termination strategy that significantly reduces the number of OPRF invocations. We provide formal security proofs under the semi-honest model and validate the protocol through extensive experiments across diverse similarity thresholds and dataset sizes. Results show that LT-PSI is significantly more efficient than brute-force threshold matching while preserving privacy. The framework naturally supports cloud-to-cloud collaborative security analytics and generalizes to broader cloud and edge threat intelligence scenarios requiring private, threshold-based feature matching.

Existing schemes that combine provable data possession (PDP) and proof of ownership (PoW) for data integrity and efficient deduplication face several practical shortcomings. First, integrity tag generation relies on data block indices or user keys, causing redundant tags for duplicate data and increasing storage overhead. Second, dynamic operations like data insertion and deletion require linear scanning, resulting in a high computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N)$</tex-math></inline-formula>. Furthermore, in multi-cloud backup environments, existing mechanisms struggle to ensure consistency between data ciphertexts and their tags. If an audit fails, locating damaged replicas requires repetitive verification by the third-party auditor (TPA), leading to high inefficiency. To overcome these issues, this paper proposes a multi-cloud data auditing mechanism (MDAM), which is based on an authenticated data structure called variable merkle hash tree (VMHT). MDAM utilizes message-derived RSA tags and secret-sharing-based proxy re-signing to achieve efficient deduplication and secure user-tag association. By integrating updatable block-level message-locked encryption (UMLE) technology with a variable branching structure, it supports dynamic updates with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log N)$</tex-math></inline-formula> complexity, saving storage and improving update efficiency. Additionally, MDAM employs a dynamic Bloom filter for efficient fault localization. Experimental results demonstrate that MDAM surpasses existing schemes in computational overhead, update efficiency, and fault localization performance.

As cloud computing advances, data owners increasingly upload large volumes of data to the cloud. Attribute-based searchable encryption (ABSE) empowers data owners to manage fine-grained access over encrypted cloud files, and supports keyword-based search for authorized users. However, current multi-owner searchable encryption schemes often suffer from efficiency limitations and vulnerabilities to keyword guessing attacks. Furthermore, access policies are typically stored in plain form, exposing confidential details about data owners and authorized users. To tackle the aforementioned issues, we put forward an expressive attribute-based searchable encryption scheme with full policy concealment. Our design leverages the reduced ordered binary decision diagram (ROBDD) for access control targeting multi-user and multi-owner environments. In our scheme, users can flexibly select data owners and utilize a single trapdoor to search across shared datasets. The integration of a warrant server that signs obfuscated keywords prevents the cloud server from launching effective keyword guessing attacks. The adoption of ROBDD enables complex access policies via boolean operations, thereby significantly enhancing the efficiency and flexibility of access control. Full policy hiding is achieved by mapping ROBDD paths to an improved bloom filter, preventing access policy leakage. We present formal definitions and security models of the proposed approach, along with rigorous security proofs. Performance evaluation is conducted through theoretical analysis and simulations. Experimental indicate that our scheme achieves superior efficiency over state-of-the-art alternatives, offering a robust solution for secure and flexible cloud data management.

Electronic health records (EHRs) have become a crucial application in cloud computing environments, necessitating advanced privacy‐preserving access control mechanisms. Ciphertext policy attribute‐based encryption (CP‐ABE) is a widely recognized solution for secure access control in outsourced data environments. However, existing CP‐ABE models face challenges related to revocation efficiency, access policy exposure, and computational burden on data owners (DOs). Even though several research works have extensively tackled this issue, most rely on re‐encryption or ciphertext updates and outsourcing strategies to proxies. However, optimization for querying all affected ciphertexts to reduce re‐encryption/ciphertext update costs is often overlooked, and the cost associated with frequent blockchain transactions for ciphertext updates and revocation records has not been addressed. Furthermore, most works do not support both attribute and user revocation efficiently. To address these issues, we propose an enhanced revocable CP‐ABE‐based access control scheme with optimized revocation performance (R‐CP‐ABE‐ORP). This scheme integrates ciphertext aggregation, lazy re‐encryption with revocation tokens, proxy‐assisted lightweight re‐encryption (PRE‐LR), blockchain, and bloom filters for fast queries to significantly improve revocation efficiency. The proposed scheme ensures forward and backward security while maintaining efficient ciphertext update and policy enforcement mechanisms. Experimental evaluations confirm that the proposed scheme outperforms related works in revocation efficiency, computational cost, and query performance.

With the advancement of vehicle-to-everything (V2X) environments and AI technology, vehicle services are continuously evolving. The vehicle-edge-cloud (VEC) network is a suitable structure for autonomous driving and AI-based vehicle services, as it enables real-time interaction between vehicles and the edge and access to cloud resources. To develop vehicle services with more diverse purposes and build high-performance AI models in the VEC network, edge servers collect increasingly large volumes of vehicle data. As the amount of data uploaded from edge servers to the cloud increases, the memory usage in the cloud also increases to accommodate the growing volume of data. Such increases in bandwidth and memory usage hinder the scalability of VEC communication. To address this issue, this paper proposes a vehicle data synchronization method-defined in this work as uploading and storing data-using an invertible Bloom filter (IBF) and its implementation using a difference- IBF (d-IBF) decoding hardware accelerator on FPGA. In the proposed method, for vehicle data with non-varying characteristics, the edge inserts the vehicle data into an IBF and uploads it to the cloud, which updates and stores the data through d- IBF decoding, thereby completing the synchronization process. This allows vehicle data with strong non-varying properties to be synchronized using minimal bandwidth, and memory usage can be reduced by storing only the d-IBF decoding results. This study analyzes real vehicle data to identify non-varying properties and devises an appropriate non-uniform quantization method to use with the proposed approach. Experiments validate the accuracy of the operations of the proposed method, including IBF programming and d-IBF decoding. Furthermore, a hardware accelerator for d-IBF decoding is designed and verified on an FPGA, proving the implementation feasibility of the proposed synchronization method.