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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. 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 actual values, mainly to escape detection. To address this limitation, we propose a detector 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.

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 1, 000, 000 geographical data, our solution to secure range query takes strictly less than a second.

Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

Classic exponent-Fourier moments (EFMs) have been popularly used for image reconstruction and invariant classification. However, EFMs lack natively the translation and scaling-invariant; in addition, they exhibit two types of drawbacks, namely numerical instability and reconstruction error, which in turn influence their reconstruction capability and image classification accuracy. This study considers the challenge of defining modified EFMs (MEFMs), which are based on modified exponent polynomials. In our methods, the basis function of traditional EFMs is appropriately modified, and these modified basis functions are used to replace the original ones. The basis function of the proposed moments is composed of piecewise modified exponent polynomials modulated by a variable parameter exponential envelope. Various types of optimal-order moments can be established by slightly adjusting the bandwidth of the modified basis functions. Finally, we extend the rotation-invariant feature of previous works and propose a new method of scaling and rotation-invariant image recognition using the proposed moments in a log-polar coordinate domain. The translation invariance can then be achieved by an image projection operation, which is substituted for the traditional approach based on the calculation of image geometric moments. The experimental results demonstrate that the MEFMs perform better than traditional EFMs and other classic orthogonal moments including the latest image moments in terms of the image reconstruction capability and the invariant recognition accuracy of smoothing filters, in both noise-free and noisy conditions.

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 (S $k$ NN) 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 S $k$ NN 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.

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.

Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

It is now cost-effective to outsource large dataset and perform query over the cloud. However, in this scenario, there exist serious security and privacy issues that sensitive information contained in the dataset can be leaked. The most effective way to address that is to encrypt the data before outsourcing. Nevertheless, it remains a grand challenge to process queries in ciphertext efficiently. In this work, we shall focus on solving one representative query task, namely dynamic skyline query, in a secure manner over the cloud. However, it is difficult to be performed on encrypted data as its dynamic domination criteria require both subtraction and comparison, which cannot be directly supported by a single encryption scheme efficiently. To this end, we present a novel framework called scale. It works by transforming traditional dynamic skyline domination into pure comparisons. The whole process can be completed in single-round interaction between user and the cloud. We theoretically prove that the outsourced database, query requests, and returned results are all kept secret under our model. Empirical study over a series of datasets demonstrates that our framework improves the efficiency of query processing by nearly three orders of magnitude compared to the state-of-the-art.

Long noncoding RNA (lncRNA) LINC00461 (LINC00461) is reported to be related to glioma progression. However, the mechanism of LINC00461 in glioma remains unclear. Expression of LINC00461, miRNA (miR)-216a, and aquaporin 4 (AQP4) was detected using real-time quantitative PCR (RT-qPCR) and western blotting. Proliferation, temozolomide (TMZ) resistance, migration, and invasion were assessed by MTT, colony formation, and transwell assays, respectively. The target binding among miR-216a, LINC00461, and AQP4 was confirmed by the luciferase reporter assay. The tumor growth was monitored in the xenograft experiment. LINC00461 was upregulated, and miR-216a was downregulated in glioma tissues and cells, and LINC00461 upregulation was correlated with large tumor size, higher WHO grade and recurrence, and poor overall survival. LINC00461 knockdown suppressed cell viability, abilities of cell cloning and migration and invasion, and TMZ resistance in glioma. Mechanically, LINC00461 was confirmed to sponge miR-216a to affect AQP4 expression. Rescue assays verified that miR-216a downregulation or AQP4 upregulation abrogated the inhibitory effect of LINC00461 knockdown on cell proliferation, migration, invasion, and TMZ resistance in vitro. Moreover, LINC00461 downregulation blocked the glioma tumor growth in vivo. In conclusion, LINC00461 knockdown inhibits glioma cell proliferation, migration, invasion, and TMZ resistance through miR-216a/AQP4 axis, suggesting LINC00461 as an oncogene in glioma progression.

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.

As many services have been subcontracted to the cloud, data in such services (i.e., management applications, signal processing, and so on) are stored on cloud in encrypted form for protecting user private information. In many emerging data and signal processing applications, approximate k-nearest neighbor (ANN) query is a prerequisite component for massive high-dimensional data (such as time series, biometric, signal, multimedia data, and so on). Unfortunately, existing secure ANN (SANN) methods encounter the limitation of dimensionality. To further resolve ANN query over high-dimensionally encrypted data, in this paper, we propose an effective SANN model in Euclidean space. In overview, a secure greedy partition method is carefully designed by applying locality sensitive hashing coding and an optimized linear order. Based on that, a novel partition-based SANN (SANNP) and a multi-division version mSANNP resolve SANN query by sequentially scanning a candidate set, which is produced by matching a cloak query with a map index. Our proposed solutions guarantee security and accuracy simultaneously, and reduce communication cost significantly. Meanwhile, the greedy partition method is proved to be indistinguishable secure under chosen-plaintext attack, which is the foundation of security for the proposed solutions. Through extensive experimental studies on four data sets, the proposed mechanisms outperform the state-of-the-art approaches and provide effective and tradeoff between result accuracy and communication cost.

In sensitive database applications (e.g., time series, scientific databases, and biometric), where database is encrypted and outsourced to a public cloud, secure approximate k-nearest neighbor (SANN) query is a fundamental research topic, aiming at retrieving high-dimensional objects that are similar to a given query from encrypted database. To process such queries without ever decrypting the data in cloud is still a challenging task. Existing works encounter various inherent limitations, such as query distinguishability, low-level efficiency and non-recoverability. All of them lead to either fragile security or low accuracy. Hence, the majority of existing works in this field are impractical for industrial applications.In this work, we present a novel model to remove the above limitations. Specifically, a reusable and single-interactive SANN paradigm is proposed in Euclidean high-dimensional space. Firstly, we present a secure variation of B+-tree (i.e., Bc-tree) to quickly locate high-dimensional candidates in cloud by leveraging on comparable encryption. Based on that, an arbitrary query requestor acquires approximate k-nearest neighbors by linearly scanning over candidates. Meanwhile, two refinements, multi-index strategy and boosting refinement strategy, are proposed to further improve the accuracy of search result and overcome the high-dependency of bandwidth, respectively. In the end, through extensive evaluations on four data sets, the proposed mechanisms are demonstrated to be superior in the tradeoff between accuracy and security.

Orthogonal moments have been extensively used as global feature-extraction descriptors in two-dimensional image reconstruction. However, a common problem for the existing image moments including orthogonal moments built in Cartesian coordinate system and radial orthogonal moments achieved at polar coordinate space is that Gibbs effect will occur, when image reconstruction is performed using features extracted from finite-order moments. It is well known that Gibbs noise can affect the capability of representation for image moments and the quality of reconstructed images. In this paper, we propose a set of novel orthogonal moments named as Walsh orthogonal moments (WOMs). The basis functions of Walsh orthogonal moments are Walsh functions, which composed of a class of complete orthogonal discontinuous binary function systems. Therefore, it provides possibility of avoiding calculating high order polynomials, and experimental results show Gibbs noise can be efficiently avoided by using discontinuous functions as the basis functions of Walsh orthogonal moments.

Database applications that manage and utilize massive data must address the issues of {element comparisons}, the core operations in index accessing, {metric computations} and {metric comparisons}, and the core operations in result ranking. In the cloud era, to avoid private information leakage, encrypted data are subcontracted, and resolutions for the problems that arise in three operations over ciphertexts are urgently required. Indeed, it is possible to handle element comparison through {order preserving encryption/encoding} (OPE) or metric computation through {homomorphic encryption} (HE) directly over ciphertexts. Unfortunately, the simultaneous achievement of both goals (i.e., metric computation and comparison) by directly combining OPE and HE remains intractable. In this work, an improved OPE, named hOPE, is proposed to support homomorphic operations over ciphertexts in addition to comparisons. Based on hOPE, AhOPE and PhOPE are designed to support homomorphic addition and product, respectively. Both schemes are proved to be indistinguishable under operated and ordered chosen-plaintext attack (IND-O\textsuperscript{2}CPA) secure when the adopted HE algorithm provides indistinguishable under ordered chosen-plaintext attack (IND-OCPA secure). hOPE is a general construction that supports arbitrary HE algorithms and achieves consistent security. We deploy AhOPE and PhOPE in practice with a trusted/untrusted third party and compare the result with the state-of-the-art methods. The results show that our presented algorithms need few interactions and fill the gap between OPE and HE.

In cloud era, it raises the intractability to keep the confidentiality and efficiency simultaneously while outsourcing encrypted data base. The secure issues are inevitable and urgent in range query (a pivotal component). Secure range query (SRQ), removing such concerns, is urgent to resolve. Concretely, a secure k-d tree (SKD tree) is constructed by invoking comparable encryption (CE) and AES, and SKD mechanism is proposed to resolve SRQ directly over the encrypted data. Until both the CE and AES are cracked, it is infeasible to reveal the query, data base or query result. In our proposal, only a single interaction is occurred between user and cloud, which promotes the searching efficiency dramatically. Experimental studies show that SKD mechanism is efficient over really geographical datasets.