Biblio

Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack. Specially, given an image selected from a group of images containing some common and salient objects, we aim to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general white-box adversarial attacks for classification, this new task faces two additional challenges: (1) low success rate due to the diverse appearance of images in the group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first blackbox joint adversarial exposure and noise attack (Jadena), where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information on the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and makes the co-salient objects undetectable. This can have strong practical benefits in properly securing the large number of personal photos currently shared on the Internet. Moreover, our method is potential to be utilized as a metric for evaluating the robustness of CoSOD methods.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Over the past two decades, itemset mining techniques have become an integral part of pattern mining in large databases. We present a novel system for mining frequent and rare itemsets simultaneously with supports weighted by cardinality in transactional datasets. Based on our neural item embedding with additive compositionality, the original mining problems are approximately reduced to polynomial-time convex optimization, namely a series of vector subset selection problems in Euclidean space. The numbers of transactions and items are no longer exponential factors of the time complexity under such reduction, except only the Euclidean space dimension, which can be assigned arbitrarily for a trade-off between mining speed and result quality. The efficacy of our method reveals that additive compositionality can be represented by linear translation in the itemset vector space, which resembles the linguistic regularities in word embedding by similar neural modeling. Experiments show that our learned embedding can bring pattern itemsets with higher accuracy than sampling-based lossy mining techniques in most cases, and the scalability of our mining approach triumphs over several state-of-the-art distributed mining algorithms.

In the present paper the exponentiation algorithm “To Center and Back” based on the idea of the additive chains exponentiation method is developed. The created by authors algorithm allows to reduce the calculation time and to improve the performance of conventional and cryptographic algorithms, as pre-quantum and quantum, and then post-quantum, in which it is necessary to use the fast exponentiation algorithm.

This paper studies the secure decentralized Pliable Index CODing (PICOD) problem, where the security constraint forbids users to decode more than one message while the decentralized setting imposes that there is no central transmitter in the system, and thus transmissions occur only among users. A converse bound from the Authors' previous work showed a factor of three difference in optimal code-length between the centralized and the decentralized versions of the problem, under the constraint of linear encoding. This paper first lists all linearly infeasible cases, that is, problems where no linear code can simultaneously achieve both correctness/decodability and security. Then, it proposes linear coding schemes for the remaining cases and shows that their code-length is to within an additive constant gap from the converse bound.

Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.

The design of public-key cryptography algorithm based on LWE hard problem is a hot topic in the field of post-quantum cryptography. In this paper, we design a new homomorphic encryption algorithm based on LWE problem. Firstly, to solve the problem that the existing encryption algorithms can only encrypt a single 0 or 1 bit, a new encryption algorithm based on LWE over integer is proposed, and its correctness and security are proved by theoretical analysis. Secondly, an additive homomorphism algorithm is constructed based on the algorithm, and the correctness of the algorithm is proved. The homomorphism algorithm can carry out multi-level homomorphism addition under certain parameters. Finally, the public key cryptography algorithm and homomorphic encryption algorithm are simulated through experiments, which verifies the correctness of the algorithm again, and compares the efficiency of the algorithm with existing algorithms. The experimental data shows that the algorithm has certain efficiency advantages.

In this paper, we propose a new provably secure cryptosystem for two party communication that provides security in the face of new technological breakthroughs. Most of the practical cryptosystems in use today can be breached in the future with new sophisticated methods. This jeopardizes the security of older but highly confidential messages. Our protocol is based on the bounded storage model first introduced in [1]. The protocol is secure as long as there is bound on the storage, however large it may be. We also suggest methods to extend the protocol to unbounded storage models where access to adversary is limited. Our protocol is a substantial improvement over previously known protocols and uses short key and optimal number of public random bits size of which is independent of message length. The smaller and constant length of key and public random string makes the scheme more practical. The protocol generates key using elements of the additive group \$\textbackslashtextbackslashmathbbZ\_\textbackslashtextbackslashmathrmn\$. Our protocol is very generalized and the protocol in [1] is a special case of our protocol. Our protocol is a step forward in making provably secure cryptosystems practical. An important open problem raised in [2] was designing an algorithm with short key and size of public random string \$O(\textbackslashtextbackslashmathcalB)\$ where \$\textbackslashtextbackslashmathcalB\$ bounds the storage of adversary. Our protocol satisfies the conditions and is easy to implement.

In recent years, the integration of encryption schemes into cyber-physical systems (CPS) has attracted much attention to improve the confidentiality of sensor signals and control input signals sent over the network. However, in principle an adversary can still modify the sensor signals and the control input signals, even though he does not know the concrete values of the signals. In this paper, we shall first show that a standard encryption scheme can not prevent some sophisticated attacks such as covert attacks, which remain invisible in the CPS with encrypted communication and a conventional diagnosis system. To cope with this problem, an improved encryption scheme is proposed to mask the communication and to cancel the influence of the attack signal out of the system. The basic idea is to swap the plaintext and the generated random value in the somewhat homomorphic encryption scheme to prevent a direct access of the adversary to the transmitted plaintext. It will be shown that the CPS with the improved encryption scheme is resilient to covert attacks. The proposed encryption scheme and the CPS structure are finally illustrated through the well-established quadruple-tank process.

Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.

A popular countermeasure against IP piracy relies on obfuscating the Finite State Machine (FSM), which is assumed to be the heart of a digital system. In this paper, we propose to use a special class of non-group additive cellular automata (CA) called D1 * CA, and it's counterpart D1 * CAdual to obfuscate each state-transition of an FSM. The synthesized FSM exhibits correct state-transitions only for a correct key, which is a designer's secret. The proposed easily testable key-controlled FSM synthesis scheme can thwart reverse engineering attacks, thus offers IP protection.

Network security is a general idea to ensure information transmission over PC and portable systems. Elliptic curve cryptosystems are nowadays widely used in public communication channels for network security. Their security relies upon the complexity of clarifying the elliptic curve discrete alogarithm issue. But, there are several general attacks in them. Elliptic bend number juggling is actualized over complex fields to enhance the security of elliptic curve cryptosystems. This paper starts with the qualities of elliptic curve cryptosystems and their security administrations. At that point we talk about limited field number-crunching and its properties, prime field number-crunching, twofold field math and complex number-crunching, and elliptic bend number-crunching over prime field and parallel field. This paper proposes how to execute the unpredictable number of math under prime field and double field utilizing java BigInteger class. also, we actualize elliptic bend math and elliptic bend cryptosystems utilizing complex numbers over prime field and double field and talk about our trials that got from the usage.

Public key infrastructures (PKIs) are one of the main building blocks for securing communications over the Internet. Currently, PKIs are under the control of centralized authorities, which is problematic as evidenced by numerous incidents where they have been compromised. The distributed, fault tolerant log of transactions provided by blockchains and more recently, smart contract platforms, constitutes a powerful tool for the decentralization of PKIs. To verify the validity of identity records, blockchain-based identity systems store on chain either all identity records, or, a small (or even constant) sized amount of data for verifying identity records stored off chain. However, as most of these systems have never been implemented, there is little information regarding the practical implications of each design's tradeoffs. In this work, we first implement and evaluate the only provably secure, smart contract based PKI of Patsonakis et al. on top of Ethereum. This construction incurs constant-sized storage at the expense of computational complexity. To explore this tradeoff, we propose and implement a second construction which, eliminates the need for trusted setup, preserves the security properties of Patsonakis et al. and, as illustrated through our evaluation, is the only version with constant-sized state that can be deployed on the live chain of Ethereum. Furthermore, we compare these two systems with the simple approach of most prior works, e.g., the Ethereum Name Service, where all identity records are stored on the smart contract's state, to illustrate several shortcomings of Ethereum and its cost model. We propose several modifications for fine tuning the model, which would be useful to be considered for any smart contract platform like Ethereum so that it reaches its full potential to support arbitrary distributed applications.

We consider some approaches to the construction of lightweight block ciphers and introduce the definitions for "index of strong nonlinearity" and "index of perfection". For PRESENT, MIDORI, SKINNY, CLEFIA, LILLIPUT mixing and nonlinear properties were evaluated. We obtain the exact values of the exponents for mixing matrices of round functions and the upper bounds for indexes of perfection and strong nonlinearity. It was determined by the experiment that each coordinate function of output block is nonlinear during 500 rounds. We propose the algorithmic realization of 16×16 S-box based on the modified additive generator with lightweight cipher SPECK as a modification which does not demand memory for storage huge substitution tables. The best value of the differential characteristic of such S-box is 18/216, the minimal nonlinearity degree of coordinate functions is equal to 15 and the minimal linear characteristic is 788/215.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.

This paper investigates practical strategies for distributing payload across images with content-adaptive steganography and for pooling outputs of a single-image detector for steganalysis. Adopting a statistical model for the detector's output, the steganographer minimizes the power of the most powerful detector of an omniscient Warden, while the Warden, informed by the payload spreading strategy, detects with the likelihood ratio test in the form of a matched filter. Experimental results with state-of-the-art content-adaptive additive embedding schemes and rich models are included to show the relevance of the results.

Membership revocation is essential for cryptographic applications, from traditional PKIs to group signatures and anonymous credentials. Of the various solutions for the revocation problem that have been explored, dynamic accumulators are one of the most promising. We propose Braavos, a new, RSA-based, dynamic accumulator. It has optimal communication complexity and, when combined with efficient zero-knowledge proofs, provides an ideal solution for anonymous revocation. For the construction of Braavos we use a modular approach: we show how to build an accumulator with better functionality and security from accumulators with fewer features and weaker security guarantees. We then describe an anonymous revocation component (ARC) that can be instantiated using any dynamic accumulator. ARC can be added to any anonymous system, such as anonymous credentials or group signatures, in order to equip it with a revocation functionality. Finally, we implement ARC with Braavos and plug it into Idemix, the leading implementation of anonymous credentials. This work resolves, for the first time, the problem of practical revocation for anonymous credential systems.

The Internet of Things is a disruptive paradigm based on the cooperation of a plethora of heterogeneous smart things to collect, transmit, and analyze data from the ambient environment. To this end, many monitored variables are combined by a data analysis module in order to implement efficient context-aware decision mechanisms. To ensure resource efficiency, aggregation is a long established solution, however it is applicable only in the case of one sensed variable. We extend the use of aggregation to the complex context of IoT by proposing a novel approach for secure cooperation of smart things while granting confidentiality and integrity. Traditional solutions for data concealment in resource constrained devices rely on hop-by-hop or end-to-end encryption, which are shown to be inefficient in our context. We use a more sophisticated scheme relying on homomorphic encryption which is not compromise resilient. We combine fully additive encryption with fully additive secret sharing to fulfill the required properties. Thorough security analysis and performance evaluation show a viable tradeoff between security and efficiency for our scheme.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.

We propose a general approach to construct cryptographic significant Boolean functions of (r + 1)m variables based on the additive decomposition F2rm × F2m of the finite field F2(r+1)m, where r ≥ 1 is odd and m ≥ 3. A class of unbalanced functions is constructed first via this approach, which coincides with a variant of the unbalanced class of generalized Tu-Deng functions in the case r = 1. Functions belonging to this class have high algebraic degree, but their algebraic immunity does not exceed m, which is impossible to be optimal when r > 1. By modifying these unbalanced functions, we obtain a class of balanced functions which have optimal algebraic degree and high nonlinearity (shown by a lower bound we prove). These functions have optimal algebraic immunity provided a combinatorial conjecture on binary strings which generalizes the Tu-Deng conjecture is true. Computer investigations show that, at least for small values of number of variables, functions from this class also behave well against fast algebraic attacks.