Symmetric searchable encryption (SSE) for geo-textual data has attracted significant attention. However, existing schemes rely on task-specific, incompatible indices for isolated specific secure queries (e.g., range or k-nearest neighbor spatial-keyword queries), limiting practicality due to prohibitive multi-index overhead. To address this, we propose RISK, a model for rich spatial-keyword queries on encrypted geo-textual data. In a textual-first-then-spatial manner, RISK is built on a novel k-nearest neighbor quadtree (kQ-tree) that embeds representative and regional nearest neighbors, with the kQ-tree further encrypted using standard cryptographic tools (e.g., keyed hash functions and symmetric encryption). Overall, RISK seamlessly supports both secure range and k-nearest neighbor queries, is provably secure under IND-CKA2 model, and extensible to multi-party scenarios and dynamic updates. Experiments on three real-world and one synthetic datasets show that RISK outperforms state-of-the-art methods by at least 0.5 and 4 orders of magnitude in response time for 1% range queries and 10-nearest neighbor queries, respectively.

The burgeoning interest in cloud computing and trusted execution environments (TEEs) has prompted a focus on enhancing the performance of diverse queries on encrypted geo-textual data within public cloud infrastructures. Secure spatial keyword range queries are pivotal for applications that rely on cloud assistance. However, current approaches to SRSK queries are inadequate for multi-user settings or demand excessive client-side resources to process queries.In this paper, we present RASKEE, a novel SRSK query model integrating conventional cryptographic techniques with TEEs. RASKEE is designed to support a multi-user environment, offloading most computational and storage demands from clients to the public cloud. Theoretically, RASKEE minimizes client-side storage and computational overheads and provably achieves IND-CKA2 security. Empirical evaluations on three real-world datasets demonstrate that RASKEE outperforms state-of-the-art solutions by up to 0.5 − 4 orders of magnitude in terms of response time for clients.

Abstract This study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale , dist-scale-s and dist-scale-e , which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

Cloud-based quantization is a key technique for deploying deep neural networks on resource-constrained devices. However, the growing number of heterogeneous devices has placed an increasing burden on the cloud server. It is necessary to handle the high costs of quantizing original models to multiple bitwidths on the cloud server. Therefore, we propose an efficient hierarchical quantization method (HQAQ), which transforms classical cloud-based quantization to two-phase quantization: cloud-edge post-training quantization and edge-device hybrid re-quantization. HQAQ first quantizes original models on the cloud server and distributes the quantized model to edge servers. Then the edge servers re-quantize the quantized model to respond to heterogeneous devices’ requests. To minimize the computational costs on edge servers, edge-device hybrid re-quantization integrates top-bitwidth quantization-aware training with cross-bitwidth quantization points transfer. Quantization points transfer employs clustering to discover the distribution of the quantization points with higher bit-width, which helps quantize the models of lower-bitwidth models. The experimental results on image classification and time series prediction tasks demonstrate that the proposed method improves communication efficiency while maintaining model performance.

Ensuring the ACID properties of transactions is the fundamental functionality of transactional DBMSs. However, through our study on existing solutions on transaction management, we found that transaction implementations in some mainstream databases, such as MySQL, MariaDB and TiDB, may violate what they claim in their documentation, in the form of incorrect database state or query results. Since there is still a lack of efficient and comprehensive testing methods to detect bugs within transaction management implementation for off-the-shelf DBMSs at present, we propose Fucci, a fuzzing framework, to solve the problem. Given a target DBMS, Fucci improves the efficiency of detecting transaction bugs through three key components: Random Conflict Construction (RCC), Multilevel Constraint Solving (MCS), and Experience-driven Automatic Simplification (EAS). RCC addresses the issue of inadequate case validity by ensuring the presence of read-write or write-write conflicts between transactions. MCS enhances the accuracy and efficiency of the transaction oracle by employing an external multi-version control system to solve data visibility. EAS is ultimately adopted to improve the efficiency of simplification and the readability of the identified bug cases. All of the above strategies are tested on commercial databases such as MySQL, MariaDB and TiDB. Accordingly, 6 previously unknown transaction bugs and 14 known duplicate transaction bugs have been newly discovered, most of which have been officially acknowledged.

Proximity graphs (PG) have gained increasing popularity as the state-of-the-art solutions to k -approximate nearest neighbor ( k -ANN) search on high-dimensional data, which serves as a fundamental function in various fields, e.g., retrieval-augmented generation. Although PG-based approaches have the best k -ANN search performance, their index construction cost is superlinear to the number of points. Such superlinear cost substantially limits their scalability in the era of big data. Hence, the goal of this paper is to accelerate the construction of PG-based methods without compromising their k -ANN search performance. To achieve this goal, two mainstream categories of PG are revisited: relative neighborhood graph (RNG) and navigable small world graph (NSWG). By revisiting their construction process, we find the issues of construction efficiency. To address these issues, we propose a new construction framework with a novel pruning strategy for edge selection, which accelerates RNG construction while keeping its k -ANN search performance. Then, we integrate this framework into NSWG construction to enhance both the construction efficiency and k -ANN search performance of NSWG. Extensive experiments are conducted to validate our construction framework for both RNG and NSWG, and that it significantly reduces the PG construction cost, achieving up to 5.6x speedup, while not compromising the k -ANN search performance.

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, and the like. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention. Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, and so on. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on the fly. SecCT+ is a solution based on trusted execution environment while SecCT is a pure software solution. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT and SecCT+.

In the era of the Internet of Things (IoT), the rapid development of cloud computing has been advancing location-based services (LBS). To enjoy the considerable advantages of lower cost and higher performance of cloud computing, it has become the first choice for most IoT enterprises to outsource data and services to public clouds. However, the privacy protection of data and the integrity of query results cannot be effectively guaranteed since public clouds cannot be fully trusted. In this paper, we propose a lightweight verifiable range query scheme (namely VeriRange). First, a pair of mutually perpendicular locality-sensitive hashing (LSH) is adopted to divide the original geographic data into subsets. Then a pivoted k dimensional (PKD) tree and a height-balanced binomial search tree (i.e., AVL tree) are established on the client and the cloud respectively to accelerate the processes of queries. To ensure the verification of query results, a keyed hash function is utilized to generate a verification tag for each subset. Verification tags in the public cloud are encrypted and independent of queries. It ensures fast verification of query results because the verification tags are not repeatedly calculated on each query. Formal security analysis shows VeriRange ensures the privacy of data, queries, and results. Experimental studies were conducted on real and synthetic datasets and demonstrated that the query and verification time in VeriRange is almost 2-3 orders of magnitude faster than that in state-of-art schemes.

Deep learning inference has become a fundamental component of cloud service providers, while privacy issues during services have received significant attention. Although many privacy-preserving schemes have been proposed, they require further improvement. In this article, we proposeSerpens, an efficient convolutional neural network (CNN) secure inference framework to protect users’ uploaded data. We introduce a pair of novel concepts, namely separable and conditional separable, to determine whether a layer in CNNs can be computed over multiple servers or not. We demonstrate that linear layers are separable and construct factor-functions to reduce their overhead to nearly zero. For the two nonlinear layers, i.e., ReLU and max pooling, we design four secure protocols based on homomorphic encryption and random masks for two- and n-server settings. These protocols are essentially different from existing schemes, which are primarily based on garbled circuits. In addition, we extensively propose a method to split the image securely. The experimental results demonstrate thatSerpensis$60\times -197\times$faster than the previous scheme in the two-server setting. The superiority ofSerpensis even more significant in the n-server setting, only less than an order of magnitude slower than performing plaintext inference over clouds.

With the rapid growth of data size, a large number of data providers outsource their private data to cloud servers to reduce the high storage and computation burdens, but it also leads to security issues such as privacy leakage. Therefore, many privacy-preserving range query schemes have been proposed. However, most of existing secure range query schemes suffer from low query efficiency and expensive computation and update overheads. To address these issues, we propose a novel Fast Range Query (FRQ) scheme for large-scale encrypted Key-Value (KV) data. First, we introduce REMIX, a space-efficient KV index data structure based on Log-Structured Merge-trees (LSM-trees), which maintains a global sorted view of KV pairs across multiple table files for efficient range queries. Besides, we exploit the write-efficiency compression strategy of LSM-trees to ensure efficient dynamic data updates. Finally, we use Czech Havas Majewski (CHM) to protect the index structure, which reduces the computation overhead and ensures the retrieval accuracy. Formal security analysis proves that our scheme can achieve an acceptable level of security. Extensive experiments demonstrate that our scheme improves the query efficiency by nearly$8\times$and update efficiency by$7\times$compared to state-of-the-art solutions over million-level datasets.

Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, etc. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention.Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, etc. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on-the-fly. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT.

In various multi-party cooperations data stored on blockchains (i.e., on-chain data) should be decentralized consistent, verifiable, traceable, and immutable. Online analytical processing (OLAP) services are critical requirements in these applications. However, OLAP performances of existing blockchain systems are much worse than those of relational databases due to the lack of SQL support. In this paper, we propose a novel SQL-empowered analytical blockchain framework, aChain. It fully provides SQL-based OLAP services, while keeping the secure characteristics. Specifically, aChain relationally reorganizes on-chain data to support full SQL executions. Then, a relational versioning scheme is designed to ensure the atomicity and consistency of transactions. Furthermore, SQL-based APIs are designed based on an execute-order-validate architecture. Finally, we demonstrate that the performance of aChain and MySQL (in both cluster and non-cluster models) is at the same level on a typical OLAP benchmark, TPC-H.

Spatial keyword queries have attracted much attention over the past decade due to the popularity of location-based services and social networks, which brings great economic benefits. Geo-textual data are encrypted-and-delegated to public clouds for efficient management and utilization while preventing potential data leakage. However, it is still challenging to solve securerangespatialkeyword queries on encrypted data since existing works are either vulnerable or inefficient. In this paper, a secure hybrid index is built to implement efficient filtering, by embedding nodes’ paths in a novel symmetrical kd-tree into inverted indexes and employing only lightweight cryptographic techniques. A concrete scheme RASK is constructed on the secure index by utilizing only a little storage and computing resources of clients. Furthermore, RASK+ is proposed based on secure virtual technology by migrating all storage burdens from clients to public clouds. Both schemes are theoretically proved to beindistinguishable under adaptive chosen keyword attacks(IND-CKA2). Through experimental evaluations on three real datasets within consistent environments, both schemes reduce the response time by about 50%-80% compared to state-of-the-art solutions (i.e., SKSE, LSKQ, etc.). The storage overheads for the cloud are also reduced by about 0.5-2 orders of magnitude.

With the extensive usage of convolutional neural networks (CNNs), privacy issues within practical applications have attracted much attention, especially when deep learning services are provided by third-party clouds. Many private inference schemes have been proposed, but their overheads are still too large. In this work, we find that the inference procedure of CNNs can be separated and performed synergistically by many parties. Following this observation, we present a pair of novel notions, namely separable and conditional separable, to tell whether a layer in CNNs can be exactly computed over multiple parties or not. Besides, we also prove that CNNs are conditionally separable. Accordingly, we propose Serpens, a private inference framework under multi-server settings. Serpens reduces the overhead of linear layers to almost zero, and now the computing bottleneck is ReLU. To address that, we design two secure ReLU protocols based on homomorphic encryption and random masks for two- and three-server settings. Experimental results show that Serpens is 78x-105x faster than the state-of-the-art private inference scheme in the two-server setting, and the superiority of Serpens is even larger in the three-server setting, only 11x-64x slower than performing the same inference over plaintext images.

For achieving the goal of two-way communication and power flows, smart grids are integrated with much state-of-the-art hardware and software. However, these newly added components also introduce a lot of vulnerabilities into the power systems, which results in that malicious users can launch various cyber–physical attacks to steal electricity. The existing electricity theft detection techniques suffer from an implicit assumption that malicious users tamper with smart meter readings to values much less than their actual electricity consumptions. These are called large-amount electricity theft (LET) attacks. Nevertheless, in the real world, some malicious users may be cautious enough to deliberately launch small-amount electricity theft (SET) attacks, where smart meter readings are manipulated to numbers slightly lower than the actual values, mainly to escape detection. To address this limitation, we propose a detector that is able to deal with both LET and SET attacks effectively. This detector applies a cumulative sum (CUSUM) control chart and a Shewhart control chart together to analyze users’ reported readings and measurements of a central observer meter. It consists of an electricity theft detection phase, which aims to detect the existence of LET/SET attacks timely and a malicious user identification phase, which aims to identify malicious users exactly. Extensive experiments are conducted to evaluate the proposed detector, and the results show that it has good performance in terms of several metrics.

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity.Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications.

In the era of cloud computing, to achieve convenient location-based service (LBS), consumers such as users, companies, and organizations prefer subcontracting massive geographical data to public clouds after encryption for privacy and security. However, numerous harmful cyber-attacks happen on those public clouds in an unpredicted and hourly manner. To alleviate those concerns, various secure query schemes on the encrypted data have been proposed in the literature. As a fundamental query of LBSs, forward-secure range query has not been well investigated. To address this issue, we propose a lightweight and forward-secure range query (LS-RQ) on geographically encrypted data, which soundly balances between security and efficiency. Promisingly, we design an index mechanism to manage geographical data on the public clouds, while not compromising the privacy of data. Moreover, our LS-RQ schemes provide a convenient approach to range query on geographically encrypted data on-the-fly. We also rigorously prove that LS-RQ is forward-secure. Finally, extensive experimental studies are performed on both real and synthetic datasets. By observation, our LS-RQ schemes are highly efficient in realistic environments. Particularly, on encrypted datasets with about 1000000 geographical data, our solution to secure range query takes strictly less than a second.

Recently, federated learning has received widespread attention, which will promote the implementation of artificial intelligence technology in various fields. Privacy-preserving technologies are applied to users’ local models to protect users’ privacy. Such operations make the server not see the true model parameters of each user, which opens wider door for a malicious user to upload malicious parameters and make the training result converge to an ineffective model. To solve this problem, in this article, we propose a poisoning attack defense framework for horizontal federated learning systems called ADFL. Specifically, we design a proof generation method for users to generate proofs to verify whether it is malicious or not. An aggregation rule is also proposed to make sure the global model has a high accuracy. Several verification experiments were conducted and the results show that our method can detect malicious user effectively and ensure the global model has a high accuracy.

Contact tracing is a critical tool in containing epidemics such as COVID-19. Researchers have carried out a lot of work on contact tracing. However, almost all of the existing works assume that their clients and authorities have large storage space and powerful computation capability and clients can implement contact tracing on their own mobile devices such as mobile phones, tablet computers, and wearable computers. With the widespread outbreaks of the epidemics, these approaches are of less robustness to a larger scale of datasets when it comes to resource-constrained clients. To address this limitation, we propose a publicly verifiable contact tracing algorithm in cloud computing (PvCT), which utilizes cloud services to provide storage and computation capability in contact tracing. To guarantee the integrity and accuracy of contact tracing results, PvCT applies a novel set accumulator-based authentication data structure whose computation is outsourced, and the client can check whether returned results are valid. Furthermore, we provide rigorous security proof of our algorithm based on the q -Strong Bilinear Diffie–Hellman assumption. Detailed experimental evaluation is also conducted on three real-world datasets. The results show that our algorithm is feasible within milliseconds of client CPU time and can significantly reduce the storage overhead from the size of datasets to a constant 128 bytes.

Due to the explosive increase of data in both the aspects of dimensionality and volume, performing k nearest neighbors search over cloud environments has been progressively receiving more attention among researchers in the field of database cloud computing. However, the key challenge for switching k nearest neighbors search from the local server (i.e., traditional way) to the third-party cloud is, that the database which always contains series of sensitive information has to be kept secret against the cloud. In this work, we present a pair of solutions towards Secure k Nearest Neighbors(SkNN) query in outsourced environments. By skillfully utilizing coarse quantization and the cryptography techniques Advanced Encryption Standard(AES) and Paillier homomorphic encryption, we construct a secure Inverted File(IVF) and compute encrypted approximate distances directly to search for high-dimensional data in the third-party cloud provider, and finally find the better tradeoff between the search quality and security. Empirical study over real datasets and practical environments validate our solutions' feasibility, completeness, and practicality. Compared to the state-of-the-art, the proposed solutions resolve the SkNN of high-dimensional data novelly, have very limited response time and provide high privacy protection on the side of both the User and the cloud provider.

P2P network is one of the most extensive network frameworks for wireless sensor network (WSN) in Internet of Things (IoT). The peers in WSN are rational and often free ride to save power of electricity and calculation, due to the fact that the usability is of great variability and unpredictability. Such a phenomenon tremendously reduces the quality of service (QoS) in WSN. Rational exchange protocol aims at promoting QoS and guaranteeing security and fairness. However, existing schemes have taken only complete information into account, which is not up to realistic environment. The peers in realistic environment indeed possess incomplete information, which is, however, still not thoroughly investigated so far. In this paper, under asymmetric information (a typical incomplete information), an entropy based incentive model is well designed based on Markov model and QoS evaluation model to help peers cooperate in WSNs. A concrete utility function with entropy is constructed to evaluate decision utility in P2P network. Finally, an entropy based rational exchange protocol is proposed based on the presented incentive model and concrete utility function, with analysis of correctness, security, fairness, and robustness, respectively. The proposed protocol can facilitate rational peers positively and sensibly participating in services and prevent free riding for rational peers. Hence, it further promotes QoS and guarantees security and fairness simultaneously in WSNs.