Biblio
A strong designated verifier signature (SDVS) is a variation of traditional digital signatures, since it allows a signer to designate an intended receiver as the verifier rather than anyone. To do this, a signer must incorporate the verifier's public key with the signing procedure such that only the intended receiver could verify this signature with his/her private key. Such a signature further enables a designated verifier to simulate a computationally indistinguishable transcript intended for himself. Consequently, no one can identify the real signer's identity from a candidate signer and a designated verifier, which is referred to as the property of signer ambiguity. A strong notion of signer ambiguity states that no polynomial-time adversary can distinguish the real signer of a given SDVS that is not received by the designated verifier, even if the adversary has obtained the signer's private key. In 2013, Islam and Biswas proposed a provably secure certificateless strong designated verifier signature (CL-SDVS) scheme based on bilinear pairings. In this paper, we will demonstrate that their scheme fails to satisfy strong signer ambiguity and must assume a trusted private key generator (PKG). In other words, their CL-SDVS scheme is vulnerable to both key-compromise and malicious PKG attacks. Additionally, we present an improved variant to eliminate these weaknesses.
In this paper, we propose principles of information control and sharing that support ORCON (ORiginator COntrolled access control) models while simultaneously improving components of confidentiality, availability, and integrity needed to inherently support, when needed, responsibility to share policies, rapid information dissemination, data provenance, and data redaction. This new paradigm of providing unfettered and unimpeded access to information by authorized users, while at the same time, making access by unauthorized users impossible, contrasts with historical approaches to information sharing that have focused on need to know rather than need to (or responsibility to) share.
True random numbers have a fair role in modern digital transactions. In order to achieve secured authentication, true random numbers are generated as security keys which are highly unpredictable and non-repetitive. True random number generators are used mainly in the field of cryptography to generate random cryptographic keys for secure data transmission. The proposed work aims at the generation of true random numbers based on CMOS Boolean Chaotic Oscillator. As a part of this work, ASIC approach of CMOS Boolean Chaotic Oscillator is modelled and simulated using Cadence Virtuoso tool based on 45nm CMOS technology. Besides, prototype model has been implemented with circuit components and analysed using NI ELVIS platform. The strength of the generated random numbers was ensured by NIST (National Institute of Standards and Technology) Test Suite and ASIC approach was validated through various parameters by performing various analyses such as frequency, delay and power.
Ransomware has become a very significant cyber threat. The basic idea of ransomware was presented in the form of a cryptovirus in 1995. However, it was considered as merely a conceptual topic since then for over a decade. In 2017, ransomware has become a reality, with several famous cases of ransomware having compromised important computer systems worldwide. For example, the damage caused by CryptoLocker and WannaCry is huge, as well as global. They encrypt victims' files and require user's payment to decrypt them. Because they utilize public key cryptography, the key for recovery cannot be found in the footprint of the ransomware on the victim's system. Therefore, once infected, the system cannot be recovered without paying for restoration. Various methods to deal this threat have been developed by antivirus researchers and experts in network security. However, it is believed that cryptographic defense is infeasible because recovering a victim's files is computationally as difficult as breaking a public key cryptosystem. Quite recently, various approaches to protect the crypto-API of an OS from malicious codes have been proposed. Most ransomware generate encryption keys using the random number generation service provided by the victim's OS. Thus, if a user can control all random numbers generated by the system, then he/she can recover the random numbers used by the ransomware for the encryption key. In this paper, we propose a dynamic ransomware protection method that replaces the random number generator of the OS with a user-defined generator. As the proposed method causes the virus program to generate keys based on the output from the user-defined generator, it is possible to recover an infected file system by reproducing the keys the attacker used to perform the encryption.
Now a days transferring of texts, documents over the internet are the tasks in common. The transferred text must be cryptographically protected so that cannot be accessed by the invaders. In the communication medium, protected data uses cryptographic techniques and random bit generators. Once the key is generated by the random generators, how well we can secure and transmit fast in the network plays a vital role by applying appropriate algorithm. As a solution, a system is developed by symmetric algorithmic approach, uses AES and Fiestel content and also implements three different ways of random generators such as pseudorandom number generator (PRNG), linear multiples of prime sequence based method and nonlinear prime methods. Multilevel encryption and decryption techniques are adopted in the solution to transfer the information over the network securely with reduced delay. This method provides very strong technique against different kinds of attacks.
Applications of true random number generators (TRNGs) span from art to numerical computing and system security. In cryptographic applications, TRNGs are used for generating new keys, nonces and masks. For this reason, a TRNG is an essential building block and often a point of failure for embedded security systems. One type of primitives that are widely used as source of randomness are ring oscillators. For a ring-oscillator-based TRNG, the true randomness originates from its timing jitter. Therefore, determining the jitter strength is essential to estimate the quality of a TRNG. In this paper, we propose a method to measure the jitter strength of a ring oscillator implemented on an FPGA. The fast tapped delay chain is utilized to perform the on-chip measurement with a high resolution. The proposed method is implemented on both a Xilinx FPGA and an Intel FPGA. Fast carry logic components on different FPGAs are used to implement the fast delay line. This carry logic component is designed to be fast and has dedicated routing, which enables a precise measurement. The differential structure of the delay chain is used to thwart the influence of undesirable noise from the measurement. The proposed methodology can be applied to other FPGA families and ASIC designs.
Damgard et al. proposed a new primitive called access control encryption (ACE) [6] which not only protects the privacy of the message, but also controls the ability of the sender to send the message. We will give a new construction based on the Learning with Error (LWE) assumption [12], which is one of the two open problems in [6]. Although there are many public key encryption schemes based on LWE and supporting homomorphic operations. We find that not every scheme can be used to build ACE. In order to keep the security and correctness of ACE, the random constant chosen by the sanitizer should satisfy stricter condition. We also give a different security proof of ACE based on LWE from it based on DDH. We will see that although the modulus of LWE should be super-polynomial, the ACE scheme is still as secure as the general public key encryption scheme based on the lattice [5].
Deep Generative Models (DGMs) are able to extract high-level representations from massive unlabeled data and are explainable from a probabilistic perspective. Such characteristics favor sequence modeling tasks. However, it still remains a huge challenge to model sequences with DGMs. Unlike real-valued data that can be directly fed into models, sequence data consist of discrete elements and require being transformed into certain representations first. This leads to the following two challenges. First, high-level features are sensitive to small variations of inputs as well as the way of representing data. Second, the models are more likely to lose long-term information during multiple transformations. In this paper, we propose a Hierarchical Deep Generative Model With Dual Memory to address the two challenges. Furthermore, we provide a method to efficiently perform inference and learning on the model. The proposed model extends basic DGMs with an improved hierarchically organized multi-layer architecture. Besides, our model incorporates memories along dual directions, respectively denoted as broad memory and deep memory. The model is trained end-to-end by optimizing a variational lower bound on data log-likelihood using the improved stochastic variational method. We perform experiments on several tasks with various datasets and obtain excellent results. The results of language modeling show our method significantly outperforms state-of-the-art results in terms of generative performance. Extended experiments including document modeling and sentiment analysis, prove the high-effectiveness of dual memory mechanism and latent representations. Text random generation provides a straightforward perception for advantages of our model.
In order to achieve more secure and privacy-preserving, a new method of cancelable palmprint template generation scheme using noise data is proposed. Firstly, the random projection is used to reduce the dimension of the palmprint image and the reduced dimension image is normalized. Secondly, a chaotic matrix is produced and it is also normalized. Then the cancelable palmprint feature is generated by comparing the normalized chaotic matrix with reduced dimension image after normalization. Finally, in order to enhance the privacy protection, and then the noise data with independent and identically distributed is added, as the final palmprint features. In this article, the algorithm of adding noise data is analyzed theoretically. Experimental results on the Hong Kong PolyU Palmprint Database verify that random projection and noise are generated in an uncomplicated way, the computational complexity is low. The theoretical analysis of nosie data is consistent with the experimental results. According to the system requirement, on the basis of guaranteeing accuracy, adding a certain amount of noise will contribute to security and privacy protection.
Random number generator is an important building block for many cryptographic primitives and protocols. Random numbers are used to initialize key bits, nonces and initialization vectors and seed pseudo-random number generators. Physical Unclonable Functions (PUFs) are a popular security primitive in cryptographic systems used for authentication, secure key storage and so on. PUFs have nature properties of unpredictability and uniqueness which is very suitable to be served as a source of randomness. In this paper we propose a new design of a true random number generator based on ring oscillator PUFs. It utilizes a self-feedback mechanism between the response and challenge of PUFs and some simple operations, mainly addition, rotation and xor, on the output of PUFs to generate truly random bits. Our design is very simple and easy to be implemented while achieving good randomness. Experiment results verified the good quality of bits generated by our design.
Mastery of the subtleties of object-oriented programming lan- guages is undoubtedly challenging to achieve. Design patterns have been proposed some decades ago in order to support soft- ware designers and developers in overcoming recurring challeng- es in the design of object-oriented software systems. However, given that dozens if not hundreds of patterns have emerged so far, it can be assumed that their mastery has become a serious chal- lenge in its own right. In this paper, we describe a proof of con- cept implementation of a recommendation system that aims to detect opportunities for the Strategy design pattern that developers have missed so far. For this purpose, we have formalized natural language pattern guidelines from the literature and quantified them for static code analysis with data mined from a significant collection of open source systems. Moreover, we present the re- sults from analyzing 25 different open source systems with this prototype as it discovered more than 200 candidates for imple- menting the Strategy pattern and the encouraging results of a pre- liminary evaluation with experienced developers. Finally, we sketch how we are currently extending this work to other patterns.
Recommender systems have become ubiquitous in online applications where companies personalize the user experience based on explicit or inferred user preferences. Most modern recommender systems concentrate on finding relevant items for each individual user. In this paper, we describe the problem of directed edge recommendations where the system recommends the best item that a user can gift, share or recommend to another user that he/she is connected to. We propose algorithms that utilize the preferences of both the sender and the recipient by integrating individual user preference models (e.g., based on items each user purchased for themselves) with models of sharing preferences (e.g., gift purchases for others) into the recommendation process. We compare our work to group recommender systems and social network edge labeling, showing that incorporating the task context leads to more accurate recommendations.
Data clustering is an important topic in data science in general, but also in user modeling and recommendation systems. Some clustering algorithms like K-means require the adjustment of many parameters, and force the clustering without considering the clusterability of the dataset. Others, like DBSCAN, are adjusted to a fixed density threshold, so can't detect clusters with different densities. In this paper we propose a new clustering algorithm based on the mutual vote, which adjusts itself automatically to the dataset, demands a minimum of parameterizing, and is able to detect clusters with different densities in the same dataset. We test our algorithm and compare it to other clustering algorithms for clustering users, and predict their purchases in the context of recommendation systems.
Context suggestion refers to the task of recommending appropriate contexts to the users to improve the user experience. The suggested contexts could be time, location, companion, category, and so forth. In this paper, we particularly focus on the task of suggesting appropriate contexts to a user on a specific item. We evaluate the indirect context suggestion approaches over a movie data collected from user surveys, in comparison with direct context prediction approaches. Our experimental results reveal that indirect context suggestion is better and tensor factorization is generally the best way to suggest contexts to a user when given an item.
During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments. We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.
Automated recommendations have become a common feature of modern online services and mobile apps. In many practical applications, the means provided for users to interact with recommender systems (e.g., to state explicit preferences or to provide feedback on the recommendations) are, however, very limited. In order to improve such systems and consequently user satisfaction, much research work has been done over the years to build richer and more intelligent user interfaces for recommender systems. In this tutorial, we provide a comprehensive overview of existing approaches to user interaction aspects of recommender systems, with a special focus on explanation interfaces. We also provide examples of real-world systems that implement advanced interaction mechanisms and discuss open challenges in the field.
The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.
Internet of Things (IoT) will be emerged over many of devices that are dynamically networked. Because of distributed and dynamic nature of IoT, designing a recommender system for them is a challenging problem. Recently, cognitive systems are used to design modern frameworks in different types of computer applications such as cognitive radio networks and cognitive peer-to-peer networks. A cognitive system can learn to improve its performance while operating under its unknown environment. In this paper, we propose a framework for cognitive recommender systems in IoT. To the best of our knowledge, there is no recommender system based on cognitive systems in the IoT. The proposed algorithm is compared with the existing recommender systems.
Artificial neural networks are complex biologically inspired algorithms made up of highly distributed, adaptive and self-organizing structures that make them suitable for optimization problems. They are made up of a group of interconnected nodes, similar to the great networks of neurons in the human brain. So far, artificial neural networks have not been applied to user modeling in multi-criteria recommender systems. This paper presents neural networks-based user modeling technique that exploits some of the characteristics of biological neurons for improving the accuracy of multi-criteria recommendations. The study was based upon the aggregation function approach that computes the overall rating as a function of the criteria ratings. The proposed technique was evaluated using different evaluation metrics, and the empirical results of the experiments were compared with that of the single rating-based collaborative filtering and two other similarity-based modeling approaches. The two similarity-based techniques used are: the worst-case and the average similarity techniques. The results of the comparative analysis have shown that the proposed technique is more efficient than the two similarity-based techniques and the single rating collaborative filtering technique.
Recently, matrix factorization has produced state-of-the-art results in recommender systems. However, given the typical sparsity of ratings, the often large problem scale, and the large number of free parameters that are often implied, developing robust and efficient models remains a challenge. Previous works rely on dense and/or sparse factor matrices to estimate unavailable user ratings. In this work we develop a new formulation for recommender systems that is based on projective non-negative matrix factorization, but relaxes the non-negativity constraint. Driven by a simple yet instructive intuition, the proposed formulation delivers promising and stable results that depend on a minimal number of parameters. Experiments that we conducted on two popular recommender system datasets demonstrate the efficiency and promise of our proposed method. We make available our code and datasets at https://github.com/christosbampis/PCMF\_release.
It is a well-known fact that nowadays access to sensitive information is being performed through the use of a three-tier-architecture. Web applications have become a handy interface between users and data. As database-driven web applications are being used more and more every day, web applications are being seen as a good target for attackers with the aim of accessing sensitive data. If an organization fails to deploy effective data protection systems, they might be open to various attacks. Governmental organizations, in particular, should think beyond traditional security policies in order to achieve proper data protection. It is, therefore, imperative to perform security testing and make sure that there are no holes in the system, before an attack happens. One of the most commonly used web application attacks is by insertion of an SQL query from the client side of the application. This attack is called SQL Injection. Since an SQL Injection vulnerability could possibly affect any website or web application that makes use of an SQL-based database, the vulnerability is one of the oldest, most prevalent and most dangerous of web application vulnerabilities. To overcome the SQL injection problems, there is a need to use different security systems. In this paper, we will use 3 different scenarios for testing security systems. Using Penetration testing technique, we will try to find out which is the best solution for protecting sensitive data within the government network of Kosovo.