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Flexible Ethernet (FlexE) over WDM transport is one of the key technologies for achieving high-speed next generation radio access network (NG-RAN). The security issue, such as optical fiber wiretap, becomes increasingly prominent. However, optical or electrical layer secure strategies cannot support higher rate and lower overhead transport, respectively. To this end, in this paper, we propose a cross-layer secure transport strategy in end-to-end FlexE over WDM networks, which leverages Universal Hashing based FlexE data block permutation and multiple parallel fiber transmission to achieve anti-eavesdropping. Different levels of attack ability are considered for measuring the impact on network security and resources utilization. Furthermore, the trade-off between efficient resources utilization and guarantee of higher level of transport security is also explored through building an integer linear programming (ILP) model. For the computation complexity of ILP model at large scale input conditions, a Security and resource-Efficiency-oriented Ant Colony optimization algorithm (SEAC) is proposed. Simulation results show that the cross-layer defense strategy is effective to struggle against intruders with different levels of attack ability, and the SEAC algorithm can achieve near-optimal performance and outperform the traditional first-fit algorithm.

Many concurrency platforms offer a processor oblivious model of computation, where the scheduler dynamically distributes work across threads. While this is convenient, it introduces non-determinism at runtime, which complicates debugging, because a program may have different outputs after each run. Leiserson et. al. [PPoPP ’12] persuaded Intel to modify its C/C++ compiler, which provided the Cilk Plus concurrency platform, to include a feature called pedigrees, which enables determinism by uniquely identifying strands with low overhead. They used pedigrees to design a DPRNG called DOTMIX, which hashes a pedigree, then mixes the result into a random number for a given strand. Improving the efficiency of DOTMIX by using a faster hash function is an open problem put forth by Leiserson et al. [PPoPP ’12]. We address this problem by introducing DOTMIX-Pro, which replaces the compression function used in DOTMIX with a faster universal hash function family called Square Hash due to Etzel et al. [CRYPTO ’99] and some of its variants, as well as other optimizations, which can be up to $$31\%$$ faster. Additionally, we introduce a generalization of Square Hash which works with arbitrary moduli.

The development of computer technology has determined the vector for the expansion of services based on the Internet and “G” technologies. The main requirements for modern services in the banking sector are security and reliability. At the same time, security is considered not only as ensuring the confidentiality and integrity of transactions, but also their authenticity. However, in the post-quantum period, US NIST specialists question the durability of modern means of providing basic security services based on symmetric and asymmetric cryptography algorithms. The increase in computing resources allows attackers to use modern threats in combination. Thus, there is a need to search for new and/or modify known algorithms for generating MAC (message authentication codes). In addition, the growth of services increases the amount of information that needs to be authenticated. Among the well-known hash algorithms, the hash functions of universal hashing are distinguished, which allow initially determining the number of collisions and their uniform distribution over the entire set of hash codes. Possibilities of modifying the cascade hashing algorithm UMAC (message authentication code based on universal hashing, universal MAC) based on the use of McEliece crypto-code construction on algebrogeometric (elliptic codes (EC), modified elliptic codes (MEC) and damaged codes (DC). This approach allows preserving the uniqueness property, in contrast to the classical UMAC scheme based on a block symmetric cipher (AES). The presented algorithms for evaluating the properties of universality and strict universality of hash codes make it possible to evaluate the security of the proposed hashing constructs based on universal hash functions, taking into account the preservation of the universality property

A description of an authenticated encryption with associated data (AEAD) block cypher mode, called XTSMAC, is presented. The results of the cryptanalysis, including the non-applicability of a new attack, based on differential properties of non-linear permutations, is also presented. The comparison with other AEAD modes and the results of practical software implementation on Intel’s CPU conclude the article.

We consider secret sharing where a dealer wants to share a secret with several participants such that predefined subsets of participants can reconstruct the secret and all other subsets of participants cannot learn any information about the secret. To this end, the dealer and the participants have access to samples of correlated random variables and a one-way (from the dealer to the participants), authenticated, public, and rate-limited communication channel. For this problem, we propose the first constructive and low-complexity coding scheme able to handle arbitrary access structures. Our construction relies on a vector quantization coupled with distribution approximations with polar codes to handle the reliability constraints, followed by universal hashing to handle the security constraints. We stress that our coding scheme does not require symmetry or degradation assumptions on the correlated random variables, and does not need a pre-shared secret among the participants and dealer. Our result is also optimal in the special case of rate-unlimited public communication when all the participants are needed to reconstruct the secret.

As the device capacity of Dynamic Random Access Memory (DRAM) increases, refresh operation becomes a significant contributory factor toward total power consumption and memory throughput of the device. To reduce the problems associated with the refresh operation, a multi-rate refresh technique that changes the refresh period based on the retention time of DRAM cells has been proposed. Unfortunately, the multi-rate refresh technique has a scalability issue, because the additional storage and logic overhead on a memory controller increases as the device capacity increases. In this article, we propose a novel redundancy repair technique to increase the refresh period of DRAM by using a universal hashing technique. Our redundancy repair technique efficiently repairs both hard faults, which occur during the manufacturing process, and weak cells that have short retention time using the remaining spare elements after the process. Also, our technique solves the Variable Retention Time problem by repairing weak cells at boot time by exploiting the Built-in self-repair (BISR) technique and Error Correction Code. Our technique outperforms a conventional BISR redundancy repair with very little hardware overhead, and ensure reliability with more extended refresh period in the entire system. In particular, our experimental results show that our BISR technique achieves 100% repair rate at a 384ms refresh period in 1.0e-6 hard fault BER configuration, and reduces the refresh energy consumption by 83.9% compared to the 64ms refresh and 12% compared to the conventional multi-rate refresh technique for the state-of-the-art 4Gb device.

Досліджено шляхи підвищення імітостійкості сигналів попередження систем охорони НГУ на об’єктах критичної інфраструктури. Виконано аналіз автентичності повідомлень з конструктивними елементами MAC кодів на основі сімей хеш-функцій. Зроблений висновок, що практичні схеми хешування повинні включати класи хеш-функцій з великим коефіцієнтом стиснення для даних можливо дуже великого об՚єму. Наведені оцінки колізійної стійкості для схем універсального хешування за найкращими алгебраїчними кривими з великим числом точок і максимальними кривими., An important part of the combat activity of units of the National Guard of Ukraine is the physical protection of important state critical infrastructure facilities. The development of modern information technologies leads to the emergence of new threats to the security of such facilities, in particular, the number and power of cyber-attacks motivated by the interests of individual states, organizations and groups of people is growing. This determines the relevance of ensuring the confidentiality and integrity of messages exchanged between units of the National Guard of Ukraine through a warning system with potentially dangerous objects. To solve this problem, imitation-resistant data encoding is used, which makes it possible to check whether the data has been changed by a third party. The likelihood that the data has been changed serves as a measure of the resistance of the cipher.Ensuring the simulated resistance of data transmission systems is possible on the basis of solving an interconnected set of information protection tasks. The quality of solving security problems is largely determined by cryptographic algorithms for encryption, digital signature, hashing and generation of authentication codes used.A study of ways to increase the spoofing resistance of warning signals of the guard systems of the National Guard of Ukraine at critical infrastructure facilities was conducted.An analysis of the authenticity of messages with constructive elements of MAC codes based on hash function families is made. It is concluded that practical hashing schemes should include hash classes with a large compression ratio for data of very large volumes as possible. For these purposes, hash families on the basis of long algebraic geometric codes are of interest.An analysis of the general characteristics of algebraic-geometric codes showed that the best properties in terms of their code distance and computational complexity are the extended Reed-Solomon code, codes on Hermite curves, and codes on Suzuki curves. Collision stability estimates are presented for universal hashing schemes for the best algebraic curves with a large number of points and a maximum curve.The presented theory of universal hashing with Reed-Solomon algebraic-geometrical codes and codes on Hermite curves shows the ability to provide the necessary measure of the probability of collision of hash functions. Since a linear increase in collision probability for Reed-Solomon hashing limits the size of the hashed message, one of the ways to increase the simplicity of data transmission is to use codes on Hermite curves.

Quantum Key Distribution (QKD) is, nowadays, the dominant practical application in the quantum technologies field. Nevertheless, extracting secure keys from a physical implementation of a QKD system remains a complicated procedure. In this paper, post-processing algorithms were implemented to extract secret keys from a Continuous-Variable QKD (CV-QKD) system. The Discrete Modulated Four State CV-QKD protocol was considered due to lower complexity in the reconciliation procedure. The secret keys were extracted considering Maximum-Likelihood Estimators (MLEs) for parameter estimation, Iterative Low-Density Parity Check (LDPC) codes complemented with the Sum-Product Algorithm and error groups iterative reconciliation for information reconciliation, and the Toeplitz matrix as the universal hash function for privacy amplification. The post-processing algorithms were applied to extract secret keys from a simulated implementation of the Discrete Modulated Four State CV-QKD protocol, with double homodyne detection.

HalftimeHash is a new algorithm for hashing long strings. The goals are few collisions (different inputs that produce identical output hash values) and high performance.

Online cipher is an important primitive in many cryptographic schemes, such as authenticated encryption schemes. Considering performance and security, the Hash-ECB-Hash (HEH) structure provides a potential way to construct parallelizable and CCA secure online cipher. In this paper, we start from the online cipher POE which is the only instantiation of Hash-ECB-Hash structure in the literature. However, the AXU property of hash function in the hash layer cannot guarantee the security of POE. Then we propose a new concept of online universal hash function (OUHF) for the hash layer and prove that the Hash-ECB-Hash structure is CCA secure, if the hash layer is online almost universal (OAU) hash function and the underlying block cipher is CCA secure. We also give several concrete constructions of OAU.

Due to the reciprocity of wireless channels, the communication parties can directly extract the shared key from channel. This solution were verified through several schemes. However, in real situations, channel sampling of legitimate transceivers might be impacted by noises and other interferences, which makes the channel states obtained by initiator and responder might be obvious different. The efficiency and even availability of physical layer key generation are thus reduced. In this paper, we propose a lightweight and efficient physical layer key generation scheme, which extract shared secret keys from channel state information (CSI). To improve the key generation process, the discrete cosine transform (DCT) is employed to reduce differences of channel states of legitimate transceivers. Then, these outputs are quantified and encoded through multi-bit adaptive quantization(MAQ) quantizer and gray code to generate binary bit sequence, which can greatly reduce the bit error rate. Moreover, the low density parity check (LDPC) code and universal hashing functions are used to achieve information reconciliation and privacy amplifification. By adding preprocessing methods in the key generation process and using the rich information of CSI, the security of communications can be increased on the basis of improving the key generation rate. To evaluate this scheme, a number of experiments in various real environments are conducted. The experimental results show that the proposed scheme can effificiently generate shared secret keys for nodes and protect their communication.

With the traffic growth with different deterministic transport and isolation requirements in radio access networks (RAN), Flexible Ethernet (FlexE) over wavelength division multiplexing (WDM) network is as a candidate for next generation RAN transport, and the security issue in RAN transport is much more obvious, especially the eavesdropping attack in physical layer. Therefore, in this work, we put forward a cross-layer design for security enhancement through leveraging universal Hashing based FlexE data block permutation and multiple parallel fibre transmission for anti-eavesdropping in end-to-end FlexE over WDM network. Different levels of attack ability are considered for measuring the impact on network security and resource utilization. Furthermore, the trade-off problem between efficient resource utilization and guarantee of higher level of security is also explored. Numerical results demonstrate the cross-layer defense strategies are effective to struggle against intruders with different levels of attack ability.

Given a CNF formula F on n variables, the problem of model counting or #SAT is to compute the number of satisfying assignments of F . Model counting is a fundamental but hard problem in computer science with varied applications. Recent years have witnessed a surge of effort towards developing efficient algorithmic techniques that combine the classical 2-universal hashing with the remarkable progress in SAT solving over the past decade. These techniques augment the CNF formula F with random XOR constraints and invoke an NP oracle repeatedly on the resultant CNF-XOR formulas. In practice, calls to the NP oracle calls are replaced a SAT solver whose runtime performance is adversely affected by size of XOR constraints. The standard construction of 2-universal hash functions chooses every variable with probability p = 1/2 leading to XOR constraints of size n/2 in expectation. Consequently, the challenge is to design sparse hash functions where variables can be chosen with smaller probability and lead to smaller sized XOR constraints. In this paper, we address this challenge from theoretical and practical perspectives. First, we formalize a relaxation of universal hashing, called concentrated hashing and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain (log m) tight bounds on variance and dispersion index and show that p = O( log(m)/m ) suffices for design of sparse hash functions from {0, 1}^n to {0, 1}^m. We then use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. A comprehensive experimental evaluation of our algorithm on 1893 benchmarks demonstrates that usage of sparse hash functions can lead to significant speedups., Full version of paper accepted in LICS2020 conference

Many concurrency platforms offer a processor oblivious model of computation, where the scheduler dynamically distributes work across threads. While this is convenient, it introduces non\hyp determinism at runtime, which complicates debugging, since it precludes repeatability. Leiserson et al. [PPoPP '12] persuaded Intel to modify its C/C++ compiler, which provided the Cilk Plus concurrency platform, to include a feature called pedigrees, which enables determinism by uniquely identifying strands with low overhead. They used pedigrees to design a DPRNG called DOTMIX, which hashes a pedigree, then mixes the result into a random number for a given strand. Improving the efficiency of DOTMIX by using a faster hash function is an open problem put forth by Leiserson et al. [PPoPP '12]. We address this problem and improve the speed of the algorithm roughly by a factor of two, without sacrificing any statistical quality. Specifically, we replace the compression function used in DOTMIX with a faster universal hash function family due to Etzel et al. [CRYPTO '99] called Square Hash.

Tweakable block ciphers (TBCs) have been established as a valuable replacement for many applications of classical block ciphers. While several dedicated TBCs have been proposed in the previous years, generic constructions that build a TBC from a classical block cipher are still highly useful, for example, to reuse an existing implementation. However, most generic constructions need an additional call to either the block cipher or a universal hash function to process the tweak, which limited their efficiency. To address this deficit, Bao et al. proposed Tweak-aNd-Tweak (TNT) at EUROCRYPT’20. Their construction chains three calls to independent keyed permutations and adds the unmodified tweak to the state in between the calls. They further suggested an efficient instantiation TNT-AES that was based on round-reduced AES for each of the permutations. Their work could prove 2n/3-bit security for their construction, where n is the block size in bits. Though, in the absence of an upper bound, their analysis had to consider all possible attack vectors with up to 2n time, data, and memory. Still, closing the gap between both bounds remained a highly interesting research question. In this work, we show that a variant of Mennink’s distinguisher on CLRW2 with O(n23n/4) data and O(23n/2) time from TCC’18 also applies to TNT. We reduce its time complexity to O(n23n/4), show the existence of a second similar distinguisher, and demonstrate how to transform the distinguisher to a key-recovery attack on from an impossible differential. From a constructive point of view, we adapt the rigorous STPRP analysis of CLRW2 by Jha and Nandi to show O(23n/4) TPRP security for TNT. Thus, we move towards closing the gap between the previous proof and attacks for TNT as well as its proposed instance. Ministry of Education (MOE) Accepted version This research has been partially supported by Nanyang Technological University in Singapore under Grant 04INS000397C230, Singapore’s Ministry of Education under Grants RG18/19 and MOE2019-T2-1-060.

Multimodal hashing approaches have gained great success on large-scale cross-modal similarity search applications, due to their appealing computation and storage efficiency. However, it is still a challenge work to design binary codes to represent the original features with good performance in an unsupervised manner. We argue that there are some limitations that need to be further considered for unsupervised multimodal hashing: 1) most existing methods drop the discrete constraints to simplify the optimization, which will cause large quantization error; 2) many methods are sensitive to outliers and noises since they use $\ell _{2}$ -norm in their objective functions which can amplify the errors; and 3) the weight of each modality, which greatly influences the retrieval performance, is manually or empirically determined and may not fully fit the specific training set. The above limitations may significantly degrade the retrieval accuracy of unsupervised multimodal hashing methods. To address these problems, in this paper, a novel hashing model is proposed to efficiently learn robust discrete binary codes, which is referred as Robust and Flexible Discrete Hashing (RFDH). In the proposed RFDH model, binary codes are directly learned based on discrete matrix decomposition, so that the large quantization error caused by relaxation is avoided. Moreover, the $\ell _{2,1}$ -norm is used in the objective function to improve the robustness, such that the learned model is not sensitive to data outliers and noises. In addition, the weight of each modality is adaptively adjusted according to training data. Hence the important modality will get large weights during the hash learning procedure. Owing to above merits of RFDH, it can generate more effective hash codes. Besides, we introduce two kinds of hash function learning methods to project unseen instances into hash codes. Extensive experiments on several well-known large databases demonstrate superior performance of the proposed hash model over most state-of-the-art unsupervised multimodal hashing methods.

The hashing method maps similar data of various types to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low-storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with for large-scale data sets: 1) discrete constraints are involved in the learning of the hash function and 2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting in both time and space complexity greater than $O(n^{2})$ . To address these issues, we propose a novel discrete supervised hash learning framework that can be scalable to large-scale data sets of various types. First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient. Second, by adopting the asymmetric low-rank matrix factorization , we propose the fast clustering-based batch coordinate descent method, such that the time and space complexity are reduced to $O(n)$ . The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks and kernel methods, as well as any types of data to hash, including images and videos. Experiments on large-scale data sets demonstrate that the proposed method is superior or comparable with the state-of-the-art hashing algorithms.

In this paper, we propose a collaborative multiview hashing (CMH) approach to incorporate multiview representations into the binary code learning for scalable visual retrieval. Unlike most existing multiview hashing methods which learn linear projections to preserve the fused similarity relationship across different views in unsupervised manner, we employ the nonlinear hashing functions as the projection in each view and exploit the diverse information of multiview representations by utilizing the collaboration between view representations and the correlation between the view representations and the semantic labels. Specifically, the binary codes in each view are constrained to be predictive to each other to exploit the collaboration between the descriptors in different views that describe the same sample. Furthermore, the binary codes in all views are enforced to preserve the semantic relationship between data samples. The hashing functions are implemented in the form of multi-layer neural network with nonlinear transformations at each layer and trained with both the view collaboration and semantic preserving constraints on the outputs. Experimental results on two datasets validate the superiority of the proposed approach in comparison with several state-of-the-art hashing methods.

As far as we know, we are the first to propose a general framework to incorporate the quantization-based methods into the conventional similarity-preserving hashing, in order to improve the effectiveness of hashing methods. In theory, any quantization method can be adopted to reduce the quantization error of any similarity-preserving hashing methods to improve their performance.This framework can be applied to both unsupervised and supervised hashing. We experimentally obtained the best performance compared to state-ofthe-art supervised and unsupervised hashing methods on six popular datasets.We successfully show it to work on a huge dataset SIFT1B (1 billion data points) by utilizing the graph approximation and out-of-sample extension. Nowadays, due to the exponential growth of user generated images and videos, there is an increasing interest in learning-based hashing methods. In computer vision, the hash functions are learned in such a way that the hash codes can preserve essential properties of the original space (or label information). Then the Hamming distance of the hash codes can approximate the data similarity. On the other hand, vector quantization methods quantize the data into different clusters based on the criteria of minimal quantization error, and then perform the search using look-up tables. While hashing methods using Hamming distance can achieve faster search speed, their accuracy is often outperformed by quantization methods with the same code length, due to the low quantization error and more flexible distance lookups. To improve the effectiveness of the hashing methods, in this work, we propose Quantization-based Hashing (QBH), a general framework which incorporates the advantages of quantization error reduction methods into conventional property preserving hashing methods. The learned hash codes simultaneously preserve the properties in the original space and reduce the quantization error, and thus can achieve better performance. Furthermore, the hash functions and a quantizer can be jointly learned and iteratively updated in a unified framework, which can be readily used to generate hash codes or quantize new data points. Importantly, QBH is a generic framework that can be integrated to different property preserving hashing methods and quantization strategies, and we apply QBH to both unsupervised and supervised hashing models as showcases in this paper. Experimental results on three large-scale unlabeled datasets (i.e., SIFT1M, GIST1M, and SIFT1B), three labeled datastes (i.e., ESPGAME, IAPRTC and MIRFLICKR) and one video dataset (UQ_VIDEO) demonstrate the superior performance of our QBH over existing unsupervised and supervised hashing methods.

A novel multidimensional hashing scheme, named the Exclusive Grouped Spatial Hashing (EGSH), which compresses repetitive spatial data into several compact tables while retaining efficient random access, is presented. EGSH represents a multi-level hashing without any losses. Moreover, EGSH compresses a group of repetitive elements into the same entry of the hash tables, while it uses a coverage table to mark the corresponding hash tables of the compressed data. Although, prior hashing work is related to hash collisions mitigation, here a full use of these collisions is obtained and therefore the spatial data compression rate is improved. The performance of exclusive grouped spatial hashing is presented in 2D and 3D graphic examples.

Hashing is a useful technique for fast nearest neighbor search due to its low storage cost and fast query speed. Unsupervised hashing aims at learning binary hash codes for the original features so that the pairwise distances can be best preserved. While several works have targeted on this task, the results are not satisfactory mainly due to the over-simplified model. In this paper, we propose a unified and concise unsupervised hashing framework, called binary multidimensional scaling , which is able to learn the hash code for distance preservation in both batch and online mode. In the batch mode, unlike most existing hashing methods, we do not need to simplify the model by predefining the form of hash map. Instead, we learn the binary codes directly based on the pairwise distances among the normalized original features by alternating minimization. This enables a stronger expressive power of the hash map. In the online mode, we consider the holistic distance relationship between current query example and those we have already learned, rather than only focusing on current data chunk. It is useful when the data come in a streaming fashion. Empirical results show that while being efficient for training, our algorithm outperforms state-of-the-art methods by a large margin in terms of distance preservation, which is practical for real-world applications.

Hashing-based methods have been widely applied in large scale image retrieval problem due to its high efficiency. In real world applications, it is difficult to require all images in a large database being labeled while unsupervised methods waste information from labeled images. Therefore, semi-supervised hashing methods are proposed to use partially labeled database to train hash functions using both the semantic and the unsupervised information. Multi-hashing methods achieve better precision-recall in comparison to single hashing method. However, current boosting-based multi-hashing methods do not improve performance after a small number of hash tables are created. Therefore, a bagging–boosting-based semi-supervised multi-hashing with query-adaptive re-ranking (BBSHR) is proposed in this paper. In the proposed method, an individual hash table of multi-hashing is trained using the boosting-based BSPLH, such that each hash bit corrects errors made by previous bits. Moreover, we propose a new semi-supervised weighting scheme for the query-adaptive re-ranking. Experimental results show that the proposed method yields better precision and recall rates for given numbers of hash tables and bits.

International audience; Boosted by recent legislations, data anonymizationis fast becoming a norm. However, as of yet no generic solutionhas been found to safely release data. As a consequence, datacustodians often resort to ad-hoc means to anonymize datasets.Both past and current practices indicate that hashing is oftenbelieved to be an effective way to anonymize data. Unfortunately,in practice it is only rarely effective. This paper is a tutorialto explain the limits of cryptographic hash functions as ananonymization technique. Anonymity set is the best privacymodel that can be achieved by hash functions. However, thismodel has several shortcomings. We provide three case studiesto illustrate how hashing only yields a weakly anonymized data.The case studies include MAC and email address anonymizationas well as the analysis of Google Safe Browsing.Boosted by recent legislations, data anonymizationis fast becoming a norm. However, as of yet no generic solutionhas been found to safely release data. As a consequence, datacustodians often resort to ad-hoc means to anonymize datasets.Both past and current practices indicate that hashing is oftenbelieved to be an effective way to anonymize data. Unfortunately,in practice it is only rarely effective. This paper is a tutorialto explain the limits of cryptographic hash functions as ananonymization technique. Anonymity set is the best privacymodel that can be achieved by hash functions. However, thismodel has several shortcomings. We provide three case studiesto illustrate how hashing only yields a weakly anonymized data.The case studies include MAC and email address anonymizationas well as the analysis of Google Safe Browsing.

When executing a program or storing data in a medical Internet of Things (mIoT) system, physical side-channels analysis, such as recent-timing, cold-reboot, and virtual-machine attacks, might obtain partial information about internal sensitive medical data/states in memory that the attacker can gain partial privacy information. Leakage-resilient cryptography has led to better implementation of many cryptographic primitives that can be proven secure against attackers who can obtain limited sensitive information about private keys , randomness , and other internal states , and therefore prevents from breaking the security. In this paper, to tolerate the sensitive information leakage in mIoT, we first present a leakage-resilient public-key encryption mechanism that is semantically secure against adaptively chosen-ciphertext attacks in the presence of key leakage under standard decisional Diffie–Hellman assumption. Our construction employs a special universal hashing in multiplicative group to provide an efficient strong extractor, and a key derivation function to derive one or more symmetric keys from a single value. Also, the plaintext space of the scheme is extended to the full domain field of group so as to provide a larger space for the message. We emphasis that our scheme can be deployed in mIoT since the limited power and energy budgets, the communication and computation cost, and the leakage attack are taken into account. Using the first scheme as a building block, we also give a protocol construction to achieve the security resilient to randomness leakage and key leakage. Our schemes feature with a shorter key size and a larger plaintext space. Concretely, the private-key contains only four elements in the finite field, and the allowable key-leakage rate is 25%, which provides a higher leakage rate than Naor Segev (leakage rate is 16.7%) and its variants. It is worth highlighting of the construction resilient to both key leakage and randomness leakage, simultaneously, and is flexible to deploy in easy-to-attack outdoor nodes such as in medical IoT and smart grids, since in these nodes the private keys and randomness are either stored or generated in outdoor privacy-aware environments.

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.

Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by exploiting auxiliary texts around images. In our method, relations between images and semantic topics are first discovered via robust collective non-negative matrix factorization. Afterwards, a unified topic hypergraph, where images and topics are represented with independent vertices and hyperedges, respectively, is constructed to model inherent high-order semantic correlations of images. Finally, hashing codes and functions are learned by simultaneously enforcing semantic consistence and preserving the discovered semantic relations. Experiments on publicly available datasets demonstrate that THH can achieve superior performance compared with several state-of-the-art methods, and it is more suitable for mobile image retrieval.

Hashing has been attracting much attention in computer vision recently, since it can provide efficient similarity comparison in massive multimedia databases with fast query speed and low storage cost. Since the distance metric is an explicit description of similarity, in this paper, a novel hashing method is proposed for image retrieval, dubbed Isometric Hashing (IH). IH aims to minimize the difference between the distance in input space and the distance of the corresponding binary codes. To tackle the discrete optimization in a computationally tractable manner, IH adopts some mathematical tricks to transform the original problem into a multi-objective optimization problem. The usage of linear-projection-based hash functions enables efficient generating hash codes for unseen data points. Furthermore, utilizing different distance metrics could produce corresponding hashing algorithms, thus IH can be seen as a framework for developing new hashing methods. Extensive experiments performed on four benchmark datasets validate that IH can achieve comparable to or even better results than some state-of-the-art hashing methods.

To overcome the barrier of storage and computation when dealing with gigantic-scale data sets, compact hashing has been studied extensively to approximate the nearest neighbor search. Despite the recent advances, critical design issues remain open in how to select the right features, hashing algorithms, and/or parameter settings. In this paper, we address these by posing an optimal hash bit selection problem, in which an optimal subset of hash bits are selected from a pool of candidate bits generated by different features, algorithms, or parameters. Inspired by the optimization criteria used in existing hashing algorithms, we adopt the bit reliability and their complementarity as the selection criteria that can be carefully tailored for hashing performance in different tasks. Then, the bit selection solution is discovered by finding the best tradeoff between search accuracy and time using a modified dynamic programming method. To further reduce the computational complexity, we employ the pairwise relationship among hash bits to approximate the high-order independence property, and formulate it as an efficient quadratic programming method that is theoretically equivalent to the normalized dominant set problem in a vertex- and edge-weighted graph. Extensive large-scale experiments have been conducted under several important application scenarios of hash techniques, where our bit selection framework can achieve superior performance over both the naive selection methods and the state-of-the-art hashing algorithms, with significant accuracy gains ranging from 10% to 50%, relatively.

Hashing techniques show significant advantage in dealing with enormous high-dimensional image and multimedia data. Specifically, learning based hashing methods attract a lot of attention from researchers thanks to its great performance in image retrieval. But discrete constraint problem of learning based hashing methods makes the optimization extremely difficult, which can be shown to be NP hard. Thus, most of learning based hashing methods relax the constraint and get a suboptimal result. Recently, some researchers propose discrete optimization hashing techniques to learn hash bits without any relaxation and achieve promising results. But, discrete optimization hashing method like Supervised Discrete Hashing (SDH) roughly renews all binary codes and leads to a time-consuming problem. In this paper, we propose an adaptive discrete cyclic coordinate descent (ACC) method to effectively solve discrete optimization problem. The specific objective of our study is to boost the efficiency of discrete hash optimization with equivalent performance. We evaluate the proposed method on image and multimedia databases: CIFAR-10, NUS-WIDE and MIRFLickr-25k and show that our method achieves speed-up over compared the state-of-the-art methods, while having on-par and in some cases even better performance.

The past decade has been marked by the (continued) explosion of diverse data content and the fast development of intelligent data analytics techniques. One problem we identified in the mid-2000s was similarity search of feature-rich data. The challenge here was achieving both high accuracy and high efficiency in high-dimensional spaces. Locality sensitive hashing (LSH), which uses certain random space partitions and hash table lookups to find approximate nearest neighbors, was a promising approach with theoretical guarantees. But LSH alone was insufficient since a large number of hash tables were required to achieve good search quality. Building on an idea of Panigrahy, our multi-probe LSH method introduced the idea of intelligent probing. Given a query object, we strategically probe its neighboring hash buckets (in a query-dependent fashion) by calculating the statistical probabilities of similar objects falling into each bucket. Such intelligent probing can significantly reduce the number of hash tables while achieving high quality. In this paper, we revisit the problem motivation, the challenges, the key design considerations of multi-probe LSH, as well as discuss recent developments in this space and some questions for further research.

We study the security of authenticated encryption based on a stream cipher and a universal hash function. We consider ChaCha20-Poly1305 and generic constructions proposed by Sarkar, where the generic constructions include 14 AEAD (authenticated encryption with associated data) schemes and 3 DAEAD (deterministic AEAD) schemes. In this paper, we analyze the integrity of these schemes both in the standard INT-CTXT (integrity of ciphertext) notion and in the RUP (releasing unverified plaintext) setting called INT-RUP notion. We present INT-CTXT attacks against 3 out of the 14 AEAD schemes and 1 out of the 3 DAEAD schemes. We then show INT-RUP attacks against 1 out of the 14 AEAD schemes and the 2 remaining DAEAD schemes. Next, we consider ChaCha20-Poly1305 and show that it is provably secure in the INT-RUP notion. Finally, we show that the remaining 10 AEAD schemes are provably secure in the INT-RUP notion.

Learning to hash is receiving increasing research attention due to its effectiveness in addressing the large-scale similarity search problem. Most of the existing hashing algorithms are focused on learning hash functions in the form of numeric quantization of some projected feature space. In this work, we propose a novel hash learning method that encodes features’ relative ordering instead of quantizing their numeric values in a set of low-dimensional ranking subspaces. We formulate the ranking-based hash learning problem as the optimization of a continuous probabilistic error function using softmax approximation and present an efficient learning algorithm to solve the problem. As a generalization of Winner-Take-All (WTA) hashing, the proposed algorithm naturally enjoys the numeric stability benefits of rank correlation measures while being optimized to achieve high precision with very compact code. Additionally, the proposed method can also be easily extended to nonlinear kernel spaces to discover ranking structures that can not be revealed in linear subspaces. We demonstrate through extensive experiments that the proposed method can achive competitive performances as compared to a number of state-of-the-art hashing methods.

Robust image hashing is a promising technique to represent image’s perceptual content. However, when it comes to image authentication, tradeoff between robustness and discrimination is a non-negligible issue. The allowed content preserving operations and sensitive malicious manipulations on images are quite subjective to human’s perception. So it needs tactics to design good hashing methods. In this paper we incorporate the novel concept of core alignment into hashing, where the proposed core alignment improves the performances of balance. First, we formulize the hashing as a supervised minimal optimization problem based on Locality Sensitive Hashing, in which p-stable distribution is exploited to maintain high dimensional locality features. Then we solve this problem by two sub-optimization problems, i.e., searching for optimal shift and searching for optimal quantization intervals. By using particle swarm optimization and simulated annealing programming approaches we develop two stochastic solutions to those two problems, respectively. Experimental results show that our proposed hashing optimizations can find optimal solutions with limited steps, and the hashing method is superior to other state-of-the-art methods in terms of authentication and robustness.

Cross-modal hashing can effectively solve the large-scale cross-modal retrieval by integrating the advantages of traditional cross-modal analysis and hashing techniques. In cross-modal hashing, preserving semantic correlation is important and challenging. However, current hashing methods cannot well preserve the semantic correlation in hash codes. Supervised hashing requires labeled data which is difficult to obtain, and unsupervised hashing cannot effectively learn semantic correlation from multi-modal data. In order to effectively learn semantic correlation to improve hashing performance, we propose a novel approach: Semi-Supervised Semantic Factorization Hashing (S3FH), for large-scale cross-modal retrieval. The main purpose of S3FH is to improve semantic labels and factorize it into hash codes. It optimizes a joint framework which consists of three interactive parts, including semantic factorization, multi-graph learning and multi-modal correlation. Then, an efficient alternating algorithm is derived for optimizing S3FH. Extensive experiments on two real world multi-modal datasets demonstrate the effectiveness of S3FH.

Hashing techniques have been widely adopted for cross-modal retrieval due to their low storage cost and fast query speed. Recently, some unimodal hashing methods have tried to directly optimize the objective function with discrete binary constraints. Inspired by these methods, the authors propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the binary codes without relaxing them. DCMH is formulated through semantic similarity reconstruction, and it learns binary codes for use as ideal features for classification. Furthermore, DCMH alternately updates binary codes for each modality, and its discrete hashing codes are learned efficiently, bit by bit, which is quite promising for large-scale datasets. To evaluate the effectiveness of the proposed discrete optimization, the authors optimize their objective function in a relax-and-threshold manner. Extensive empirical results on both image-text and image-tag datasets demonstrate that DCMH is a significant improvement over previous approaches in terms of training time and retrieval performance.

We present a new method to generate efficient multi-level hashing codes for image retrieval based on the deep siamese convolutional neural network (DSCNN). Conventional deep hashing methods trade off the capability of capturing highly complex and nonlinear semantic information of images against very compact hash codes, usually leading to high retrieval efficiency but with deteriorated accuracy. We alleviate the restrictive compactness requirement of hash codes by extending them to a two-level hierarchical coding scheme, in which the first level aims to capture the high-level semantic information extracted by the deep network using a rich encoding strategy, while the subsequent level squeezes them to more global and compact codes. At running time, we adopt an attention-based mechanism to select some of its most essential bits specific to each query image for retrieval instead of using the full hash codes of the first level. The attention-based mechanism is based on the guides of hash codes generated by the second level, taking advantage of both local and global properties of deep features. Experimental results on various popular datasets demonstrate the advantages of the proposed method compared to several state-of-the-art methods.

We propose a novel hashing scheme, namely DH, for high-dimensional video data.Feature disaggregation is proposed by exploiting correlations among dimensions.Hash function is learned independently on feature clusters by greedy optimization.DH can significantly reduce the computational complexity and memory usage.DH outperforms the state-of-the-art methods on three realistic action datasets. Learning based hashing methods, which aim at learning similarity-preserving binary codes for efficient nearest neighbor search, have been actively studied recently. A majority of the approaches address hashing problems for image collections. However, due to the extra temporal information, videos are usually represented by much higher dimensional (thousands or even more) features compared with images, causing high computational complexity for conventional hashing schemes. In this paper, we propose a simple and efficient hashing scheme for high-dimensional video data. This method, called Disaggregation Hashing (DH), exploits the correlations among different feature dimensions. An intuitive feature disaggregation method is first proposed, followed by a novel hashing algorithm based on different feature clusters. Additionally, a kernelized version of DH is proposed for better performance. We demonstrate the efficiency and effectiveness of our method by theoretical analysis and exploring its application on action retrieval from video databases. Extensive experiments show the superiority of our binary coding scheme over state-of-the-art hashing methods.

In the past few years, researchers have introduced several sorting algorithms to enhance time complexity, space complexity, and stability. A double hashing methodology first collects statistics about element distribution and then maps between elements of the array and indexes based on the knowledge collected during the first hashing.

In recent years, learning based hashing becomes an attractive technique in large-scale image retrieval due to its low storage and computation cost. Hashing methods map each high-dimensional vector onto a low-dimensional hamming space by projection operators. However, when processing high dimensional data retrieval, many existing methods including hashing cost a majority of time on projection operators. In this paper, we solve this problem by implementing a sparsity regularizer. On one hand, due to the sparse property of the projection matrix, our method effectively lower both the storage and computation cost. On the other hand, we reduce the effective number of parameters involved in the learned projection matrix according to sparsity regularizer, which helps avoid overfitting problem. Without relaxing binary constraints, an iterative scheme jointly optimizing the objective function directly was given, which helps to obtain effective and efficient binary codes. We evaluate our method on three databases and compare it with some state-of-the-art hashing methods. Experimental results demonstrate that our method outperforms the comparison approaches.

Hashing based approximate nearest neighbors (ANN) search has drawn considerable attraction owing to its low-memory storage and hardware-level logical computing which is doomed to be greatly applicable to quantities of large-scale and practical scenarios, such as information retrieval, computer vision and natural language processing. However, most existing hashing methods concentrate either on images only or on pairwise image-texts (labels, short documents) and rarely utilize more common sentences. In this paper, we propose D iversity R egularized L atent S emantic M atch for H ashing (DRLSMH), a new multimodal hashing method that projects images and sentences into a shared latent semantic space with label-supervised semantic constraints to proceed on multimodal retrieval. Notably, soft orthogonality is induced as a novel regularizer to preserve diverse hashing functions for compact and accurate representations; what's more, this kind of regularization also benefits the derivations of closed-form solutions with some proper relaxations under iterative optimization framework. Extensive experiments on two public datasets demonstrate the advantages of our method over some state-of-the-art baselines under cross-modal retrieval both on image-query-image, image-query-text and text-query-image tasks.

Fast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over unsupervised methods. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies may be inefficient when confronted with large datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as the dataset continues to grow and new variations appear over time. To handle these issues, we propose OSH: an Online Supervised Hashing technique that is based on Error Correcting Output Codes. We consider a stochastic setting where the data arrives sequentially and our method learns and adapts its hashing functions in a discriminative manner. Our method makes no assumption about the number of possible class labels, and accommodates new classes as they are presented in the incoming data stream. In experiments with three image retrieval benchmarks, our method yields state-of-the-art retrieval performance as measured in Mean Average Precision, while also being orders-of-magnitude faster than competing batch methods for supervised hashing. Also, our method significantly outperforms recently introduced online hashing solutions.

Due to the query effectiveness and efficiency, cross-modal similarity search based on hashing has acquired extensive attention in the multimedia community. Most existing methods do not explicitly employ the ranking information when learning hash functions, which is quite important for building practical retrieval systems. To solve this issue, this paper proposes a rank-order preserving hashing (RoPH) method with a novel regression-based rank-order preserving loss that has provable large margin property and is easy to optimize. Moreover, we jointly learn the binary codes and hash functions instead of using any relaxation trick. To solve the induced optimization problem, the alternating descent technique is adopted and each subproblem can be solved conveniently. Specifically, we show that the involved binary quadratic programming subproblem with respect to an introduced auxiliary binary variable satisfies submodularity, enabling us to use the off-the-shelf graph-cut algorithms to solve it exactly and efficiently. Extensive experiments on three benchmarks demonstrate that RoPH significantly improves the ranking quality over the state of the arts.

Existing hashing methods normally define certain specific forms of hash functions, after which an objective function can be formulated to optimize the loss on training set to learn the parameters. However, in this way, the hash function will be tightly coupled with the generated objective in most cases. Moreover, since the objectives are generally formulated with binary quantization, most of them are nonconvex, which makes the optimization difficult and consequently decreases the similarity preserving performance of hashing. To solve this problem, we propose a novel pairwise correlation preserving framework to learn compact binary codes for hashing. First, we project each data into a metric space and represent it as a vector encoding the underlying local and global structure by pairwise correlation learning. Afterwards, pairwise correlation reconstruction (PCR), is further proposed to preserve the correlations of data between the metric space and the hamming space to learn binary codes. The PCR model is convex. Moreover, no specific hash functions are needed to be predefined and the steps of correlation learning and reconstruction are independent. The above characteristics make the optimization of PCR easily and efficiently, and thus leads to better preservation of data similarity in hamming space.

Recent years have witnessed more and more success of hash methods for building efficient classifiers, but less for prediction in machine learning. In this paper, we propose a hash based method for large scale nonparallel support vector machine prediction(HNPSVM). Our key idea of this method is that we use an approximal decision function instead of exact decision function by computing the Hamming distance between hashing the normal to the hyperplane of the classifier and the features. This method benefits nonparallel support vector(NPSVM) prediction in three aspects. First, it enhances the prediction accuracy using an flexible and general method. Second, the proposed HNPSVM reduce storage cost owing to the compact binary hash representation. Last, HNPSVM can speed up the computation of classification function. Moreover, we prove that the classification results of a hash based NPSVM classifier converge to the results of the exact NPSVM classifier as the number of binary hash functions tends to infinity. Several experiments on large scale data sets show the efficient of our method.

Digital forensic tool is a software used by digital evidence investigators to extract data and information from a digital evidence. The integrity of the digital evidence must be maintained through the chain of custody in order to be admissible in court. Most digital extraction tool use either MD5 (Message Digest) or SHA (Secured Hash Algorithm) hashing to check the integrity of digital evidence. The hashing algorithm has been found to have a weakness known as collision in which two different messages have the same hashing values. Although the probability of producing such weakness is very small, this collision can be used to deny the usage of the evidence in court of justice. After the first collision has been found, many cryptanalysts have tried to explore various methods to detect the collisions with shorter and efficient time. This paper is to review the existing methods in digital forensic tools that have been used to create a collision attacks in digital evidence.

Hash-based methods can achieve a fast similarity search by representing high-dimensional data with compact binary codes. However, the spatial structure in row images was always lost in most previous methods. In this paper, a novel Locally Linear Spatial Pyramid Hash(LLSPH) algorithm is developed for the task of fast image retrieval. Unlike the conventional approach, the spatial extent of image features is exploited in our method. The spatial pyramid structure is used both to construct binary hash codes and to increase the discriminability of the description. To generate interpretable binary codes, the proposed LLSPH method captures the spatial characteristics of the original SPM and generates a low-dimensional sparse representation using multi-dictionaries Locality-constrained Linear Coding(MD_LLC). LLSPH then converts the low-dimensional data into Hamming space by the TF-IDF binarization rule. Our experimental results show that our LLSPH method can outperform several state-of-the-art hashing algorithms on the Caltech256 and ImageNet-500 datasets.

Most existing supervised hashing methods learn the affinity-preserving binary codes to represent the high-dimensional data. However, each hashing code is assumed as independent and irrelevant with other codes. In practice, the authors find that there exists context association among hashing bits. This study proposes a novel hashing method dubbed kernelised supervised context hashing, which considers the hashing codes interrelation to reduce the quantisation. In this work, the kernel formulation is employed to tackle the high-dimensional data which is mostly linear inseparable first; and then different distributions are utilised to describe the binary codes context; finally, the hashing codes can be approximated by gradient descent method iteratively. Therefore, the correlation between the hash codes is integrated to redefine the metric measurement (i.e. Hamming affinity) to preserve the data similarity in the raw space. The authors evaluate the proposed method on three image benchmarks CIFAR-10, MNIST and NUS-WIDE for image retrieval, and experimental results show that it achieves better performance than several other state-of-the-art methods.