Hashing Technique Research Articles

An efficient LSH indexing on discriminative short codes for high-dimensional nearest neighbors

In massive multimedia era, the dimension curse and the I/O performance bottleneck have become two major challenges for disk-based Approximate Nearest Neighbor (ANN) search. Hashing is a popular solution to overcome the dimension curse, one promising hashing technique is Locality Sensitive Hashing (LSH). However, most existing LSH indexings incur significant I/O cost during the search due to their low NN candidate hits in each I/O access. We recommend a novel method SC-LSH (SortingCodes-LSH) which combines LSH with another hashing technique (i.e., the discriminative short codes) to lift the hit of NN candidates so as to further boost the ANN search performance. Firstly, we intensify an LSH index and sort all the compound hashing keys according to a linear order to make similar NN candidates distributed locally. Then we generate product quantization (PQ) codes to use them as candidates instead of the original data points. These space-efficient short codes can enable us acquire significantly candidates via much less I/O operations. Moreover, based on theoretical and empirical studies among series of space-filling curves, we finally choose the Gray curve as the linear order to produce better local distribution of candidate data. All these above significantly increase the NN hits during each I/O, which greatly reduce the amount of necessary I/O access. Meanwhile, with the good similarity preserving ability, PQ codes are precise enough to discriminate NNs and thus guarantee the accuracy. Empirical study demonstrates that, comparing with four state-of-the-arts, SC-LSH achieves the best accuracy with significantly smaller I/O cost and space consumption. In fact, depending on the datasets, the I/O cost (resp., space consumption) of our scheme is only 5%-20% (resp., 1%-20%) of the other methods.

Large scale image retrieval with DCNN and local geometrical constraint model

Image retrieval, which refers to browse, search and retrieve the images of the same scene or object from a large database of digital images, has attracted increasing interests in recent years. This paper proposes a coarse-to-fine method for fast indexing with Deep Convolutional Neural Network(DCNN) and Local Geometrical Constraint Model. We first use a vector quantized DCNN feature descriptors and exploit enhanced Locality-sensitive hashing(LSH) techniques for fast coarse-grained retrieval. Then, we focus on obtaining high-precision preserved matches for fine-grained retrieval. This is formulated as a maximum likelihood estimation of a Bayesian model with latent variables indicating whether matches in the putative set are inliers or outliers. We impose the non-parametric global geometrical constraints on the correspondence using Tikhonov regularizers in a reproducing kernel Hilbert space. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the Expectation Maximization algorithm. Extensive experiments on real near-duplicate images for both feature matching and image retrieval demonstrate that the results of the proposed method outperform current state-of-the-art methods.

Password security by encryption using an extended ADFGVX cipher

Login will be the critical step for most of the web users for the user authentication. As hackers utilizing a variety of techniques to steal the passwords, it is recommended to offer secured transmission of passwords to the servers from the client system. Current web developers are using common traditional hashing techniques for securing passwords over the network. However, hashing techniques are vulnerable to several attacks like brute-force attack, dictionary attack, and birthday attack. This paper implemented password security with an easy and robust transposition based encryption technique using an extended ADFGVX cipher. The proposed cipher uses two 7 × 6 Polybius squares for encryption to accommodate common characters of the password along with the random key for encryption. Finally, this study considered four metrics for performance evaluation and compared the proposed method with the state-of-art techniques. It is found that the proposed method performed excellently regarding complexity for cracking password and satisfactorily concerning execution time.

Personalized Location Recommendation System Personalized Location Recommendation System

Location acquisition and wireless communication technologies are growing in location-based social networks. With the rapid development of location-based social networks (LBSNs), location recommendation has become an important for helping users to discover interesting locations. Most current studies on spatial item recommendations do not consider the sequential influence of locations. The authors proposed a personalized location recommendation system as a probabilistic generative model that aims to mimic the process of human decision-making when visiting locations. In this system, three tasks are involved, such as: extracting user's personal interests; extracting sequential influence; and combining them into unified networks. This system utilizes data collected from LBSNs to model a user's behavior and locations with real datasets, and it determines a user's preferred locations using collaborative filtering and a Locality Sensitive Hashing (ALSH) technique. It overcomes the challenges of the user's check-in data in LBSNs having a low sampling rate in both space and time and a huge prediction space.

Blockchain Empowered Arbitrable Data Auditing Scheme for Network Storage as a Service

The maturity of network storage technology drives users to outsource local data to remote servers. Since these servers are not reliable enough for keeping users’ data, remote data auditing mechanisms are studied for mitigating the threat to data integrity. However, many traditional schemes achieve verifiable data integrity for users only without resolutions to data possession disputes, while others depend on centralized third-party auditors (TPAs) for credible arbitrations. Recently, the emergence of blockchain technology promotes inspiring countermeasures. In this article, we propose a decentralized arbitrable remote data auditing scheme for network storage service based on blockchain techniques. We use a smart contract to notarize integrity metadata of outsourced data recognized by users and servers on the blockchain, and also utilize the blockchain network as the self-recording channel for achieving non-repudiation verification interactions. We also propose a fairly arbitrable data auditing protocol with the support of the commutative hash technique, defending against dishonest provers and verifiers. Additionally, a decentralized adjudication mechanism is implemented by using the smart contract technique for creditably resolving data possession disputes without TPAs. The theoretical analysis and experimental evaluation reveal its effectiveness in undisputable data auditing and the limited requirement of costs.

Image perception hash and structural similarity fusion model for parking lot status recognition

In order to improve the recognition accuracy of parking space change, a new fusion model is proposed. The image-aware hash technique and image structure similarity algorithm are combined to constru...

Trustworthy Electronic Voting Using Adjusted Blockchain Technology

The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies. The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This paper suggests a framework by using effective hashing techniques to ensure the security of the data. The concept of block creation and block sealing is introduced in this paper. The introduction of a block sealing concept helps in making the blockchain adjustable to meet the need of the polling process. The use of consortium blockchain is suggested, which ensures that the blockchain is owned by a governing body (e.g., election commission), and no unauthorized access can be made from outside. The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms’ utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method. This paper claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.

Asymmetric Deep Semantic Quantization for Image Retrieval

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as \textbf{\underline{A}}symmetric \textbf{\underline{D}}eep \textbf{\underline{S}}emantic \textbf{\underline{Q}}uantization (\textbf{ADSQ}). \textbf{ADSQ} is implemented using three stream frameworks, which consist of one \emph{LabelNet} and two \emph{ImgNets}. The \emph{LabelNet} leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two \emph{ImgNets}, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two \emph{ImgNets} are used to generate discriminative compact hash codes. Specifically, the function of the \emph{LabelNet} is to capture rich semantic information that is used to guide the two \emph{ImgNets} in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, \textbf{ADSQ} can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed \textbf{ADSQ} can outperforms current state-of-the-art methods.

IoT Storage Optimisation Security Framework Based on a Secure Hash Algorithm

The purpose of this research is to examine the risks that IoT applications encounter throughout operational operations, data routing, and energy utilisation, as well as potential solutions to these problems utilising Blockchain technology. This study is broken up into four parts: finding a secure hash technique for the huge volumes of data produced by IoT devices; improving storage; using cluster-based routing to keep out unwanted visitors; and safeguarding IoT programmes. The trust of the blockchain node to existing applications for the Internet of Things cannot be assured, and a sizable amount of communication assets would be used in the process of achieving consensus using the proposed security evolution method of Hybrid Cryptographic Hash Function (HCHF). Private Consensus Bitcoin with Hybrid Cryptographic Hash Function (HCHF) Enhanced Trust on Multilink was a newly developed consensus method in this study. The following are some of the key features of the IoT applications that make use of the suggested HCHF method: 1) The confidence of the nodes in the IoT application grew as a result of the development of the trust attribute mechanism, leading to the formation of a grouping structure within the network. The trust in the system is boosted by the grouping adjustment mechanism. Second, the complexity of communication is considerably simplified by categorising nodes according to their level of trust. The experimental findings show that the system's communication effectiveness and overall trust may be greatly increased by the blockchain's application of the upgraded method.

MsCRUSH: Fast Tandem Mass Spectral Clustering Using Locality Sensitive Hashing.

Large-scale proteomics projects often generate massive and highly redundant tandem mass spectra. Spectral clustering algorithms can reduce the redundancy in these data sets and thus speed up database searching for peptide identification, a major bottleneck for proteomic data analysis. The key challenge of spectral clustering is to reduce the redundancy in the MS/MS spectra data while retaining sufficient sensitivity to identify peptides from the clustered spectra. We present the software msCRUSH, which implements a novel spectral clustering algorithm based on the locality sensitive hashing technique. When tested on a large-scale proteomic data set consisting of 23.6 million spectra (including 14.4 million spectra of charge 2+), msCRUSH runs 6.9-11.3 times faster than the state-of-the-art spectral clustering software, PRIDE Cluster, while achieving higher clustering sensitivity and comparable accuracy. Using the consensus spectra reported by msCRUSH, commonly used spectra search engines MSGF+ and Mascot can identify 3 and 1% more unique peptides, respectively, compared with the identification results from the raw MS/MS spectra at the same false discovery rate (1% FDR) of peptide level. msCRUSH is implemented in C++ and is released as open-source software.

Deep Attention-Guided Hashing

With the rapid growth of multimedia data (e.g., image, audio and video etc.) on the web, learning-based hashing techniques such as Deep Supervised Hashing (DSH) have proven to be very efficient for large-scale multimedia search. The recent successes seen in Learning-based hashing methods are largely due to the success of deep learning-based hashing methods. However, there are some limitations to previous learning-based hashing methods (e.g., the learned hash codes containing repetitive and highly correlated information). In this paper, we propose a novel learning-based hashing method, named Deep Attention-guided Hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea is to use guided hash codes which are generated by the hashing network of the first stream framework (called first hashing network) to guide the training of the hashing network of the second stream framework (called second hashing network). Specifically, in the first network, it leverages an attention network and hashing network to generate the attention-guided hash codes from the original images. The loss function we propose contains two components: the semantic loss and the attention loss. The attention loss is used to punish the attention network to obtain the salient region from pairs of images; in the second network, these attention-guided hash codes are used to guide the training of the second hashing network (i.e., these codes are treated as supervised labels to train the second network). By doing this, DAgH can make full use of the most critical information contained in images to guide the second hashing network in order to learn efficient hash codes in a true end-to-end fashion. Results from our experiments demonstrate that DAgH can generate high quality hash codes and it outperforms current state-of-the-art methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.

Enhancing the security and performance of nodes in Internet of Vehicles

SummaryInternet of Vehicles is recorded as an ever growing area for connected vehicles to exchange their information with other vehicles using vehicle‐to‐vehicle communication. Vehicle‐to‐vehicle communication is possible by forming vehicular ad hoc networks, with the help of roadside units using vehicle‐to‐roadside communications in the network. Internet of networks has many advantages such as road safety, traffic management, and sharing information on daily traffic and updating traffic information. For example, the Internet of Vehicles can be used to reduce traffic and deaths occurring due to road accidents, to reduce the fuel needed, and to reduce travel time. Interconnected vehicles rapidly learn about road conditions and traffic and respond to the driver; thus, necessary actions are taken. However, the attacker can modify the information in the connected vehicles and, thus, creates problem on the road. Data fascination is one of the main attacks on connected vehicles where connected vehicles take action based on information from the other vehicle. In this paper, first, a model has been proposed to detect the data fascination attack using the hashing technique to enhance the security in the interconnected vehicles by adjusting the size of the contention window to transfer original information to other interconnected vehicles at the correct time. Second, a model has been suggested to reduce the travel time in case of traffic congestion. The efficiency of the proposed model is checked using numerical methods obtained from the simulation results. From the obtained results, the proposed approach prevents data fascination attacks in interconnected vehicles and provides high throughput with lower delay.

Time-aware distributed service recommendation with privacy-preservation

As a promising way to extract insightful information from massive data, service recommendation has gained ever-increasing attentions in both academic and industrial areas. Recently, the Locality-Sensitive Hashing (LSH) technique is introduced into service recommendation to pursue high recommendation efficiency and the capability of privacy-preservation, especially when the historical service quality (QoS) data used to make recommendation decisions are distributed across different platforms. However, existing LSH-based service recommendation approaches often face the following challenge: they often assume that the QoS data for service recommendation are static and unique, without considering the influence of dynamic context (e.g., time) on QoS. In view of this challenge, we extend the traditional LSH technique to incorporate the time factor and further propose a novel time-aware and privacy-preserving service recommendation approach based on LSH. Finally, we conduct extensive experiments on a large-scale real-world dataset, i.e., WS-DREAM, to validate the effectiveness and efficiency of our proposal. The experiment results show that our approach achieves a good tradeoff between recommendation accuracy and efficiency while guaranteeing privacy-preservation.

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

TPM

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern; and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model ( TPM ), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users’ spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top- k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

Discrete Spectral Hashing for Efficient Similarity Retrieval.

To meet the required huge data analysis, organization, and storage demand, the hashing technique has got a lot of attention as it aims to learn an efficient binary representation from the original high-dimensional data. In this paper, we focus on the unsupervised spectral hashing due to its effective manifold embedding. Existing spectral hashing methods mainly suffer from two problems, i.e., the inefficient spectral candidate and intractable binary constraint for spectral analysis. To overcome these two problems, we propose to employ spectral rotation to seek a better spectral solution and adopt the alternating projection algorithm to settle the complex code constraints, which are therefore named as Spectral Hashing with Spectral Rotation and Alternating Discrete Spectral Hashing, respectively. To enjoy the merits of both methods, the spectral rotation technique is finally combined with the original spectral objective, which aims to simultaneously learn better spectral solution and more efficient discrete codes and is called as Discrete Spectral Hashing. Furthermore, the efficient optimization algorithms are also provided, which just take comparable time complexity to existing hashing methods. To evaluate the proposed three methods, extensive comparison experiments and studies are conducted on four large-scale data sets for the image retrieval task, and the noticeable performance beats several state-of-the-art spectral hashing methods on different evaluation metrics.

HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION

The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system.

Weakly Supervised Multimodal Hashing for Scalable Social Image Retrieval

Recent years have witnessed a dramatic increase in the number of community-contributed images. Hashing-based similarity searches for social images have been attracting considerable interest from computer vision and multimedia communities due to their computational and memory efficiency. In this paper, we propose a novel weakly supervised hashing method named weakly supervised multimodal hashing, for scalable social image retrieval. Semantic-aware hash functions are learned by jointly leveraging the weakly supervised tag information and visual information. Specifically, because user-provided tags associated with social images can describe the semantic information, the hash functions are learned by exploring the semantic structure. Unfortunately, the user-provided tags are imperfect. To avoid overfitting the weakly supervised tags, the local discriminative structure and the geometric structure in the visual space are explored. Besides, to learn compact and non-redundant hash codes, the hash functions are constrained to be orthogonal and an information theoretic regularization based on the maximum entropy principle is introduced to maximize the information provided by each hash code. The learned hash functions are orthogonal, which can avoid redundancy in the learned hash codes as much as possible. The proposed hashing learning problem is formulated as the eigenvalue problem, which can be solved efficiently. Extensive experiments are conducted on two widely used social image data sets and the encouraging performance compared with the state-of-the-art hashing techniques demonstrates the effectiveness of the proposed method.

Signature Verification and Bloom Hashing Technique for Efficient Cloud Data Storage

Cloud computing is a one of the network-based computing that provides services to computer and other cloud devices. Secure cloud data storage is a significant when using the cloud services provided by the service provider in cloud environment. Many research works have been developed to provide secure wireless communication in cloud. However, integrity verification of stored data on cloud was not addressed in an efficient manner. In order to overcome above limitations, bloom hashing based Schnorr signature (BH-SS) technique is proposed. In BH-SS technique, at first, the cloud users send the request to cloud server and it response the user requested data. Next, bloom hash function is used to increase the integrity of cloud data storage. Bloom hash function is an efficient data structure used for storing the data and its hashed value. Bloom hash function stores the various cloud user data in it bits of array by generating hash value for each cloud data. Each data has the diverse hash value in bit array. This helps for BH-SS technique to improve the data integrity and dynamic data storage in cloud environment. Lastly, BH-SS technique employed Schnorr signature to ensure confidentiality of stored data with the aid of producing private signing key and public verification key. Therefore, proposed BH-SS technique improves the data integrity rate and reduces execution time. The BH-SS technique conducts experimental works on the parameters such as data integrity, space complexity, execution time and data confidentiality rate. The experimental results observe that the BH-SS technique is able to enhance the cloud data integrity and also reduces the execution time of cloud storage and space complexity when compared to state-of-the-art-works.

Locally private Jaccard similarity estimation

SummaryJaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP‐JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.