Secret sharing is a useful method for secure distributed data storage. Such a distributed data storage can avoid the information leakage under an attack to a limited number of distributed servers. While such distributed servers send their shares to an end user according to the request, there is a risk that malicious distributed servers send incorrect shares. To detect or identify such malicious servers, we need verifiable secure distributed data storage, which can be constructed from verifiable secret sharing. However, many of previous protocols for verifiable secret sharing are constructed in a specific form. This paper proposes an adaptive construction of verifiable secret sharing, which uses an existing secret sharing protocol as a subprotocol. Also, our method can be applied to any existing secret sharing protocol. This type construction realizes an economical construction.

The guesswork of a quantum ensemble quantifies the minimum number of guesses needed in average to correctly guess the state of the ensemble, when only one state can be queried at a time. Here, we derive analytical solutions of the guesswork problem subject to a finite set of conditions, including the analytical solution for any qubit ensemble with uniform probability distribution. As explicit examples, we compute the guesswork for any qubit regular polygonal and polyhedral ensemble.

BACKGROUND: Proteins are integral part of all living beings, which are building blocks of many amino acids. To be functionally active, amino acids chain folds up in a complex way to give each protein a unique 3D shape, where a minor error may cause misfolded structure. Genetic disorder diseases i.e. Alzheimer, Parkinson, etc. arise due to misfolding in protein sequences. Thus, identifying patterns of amino acids is important for inferring protein associated genetic diseases. Recent studies in predicting amino acids patterns focused on only simple protein misfolded disease i.e. Chromaffin Tumor, by association rule mining. However, more complex diseases are yet to be attempted. Moreover, association rules obtained by these studies were not verified by usefulness measuring tools. RESULTS: In this work, we analyzed protein sequences associated with complex protein misfolded diseases (i.e. Sickle Cell Anemia, Breast Cancer, Cystic Fibrosis, Nephrogenic Diabetes Insipidus, and Retinitis Pigmentosa 4) by association rule mining technique and objective interestingness measuring tools. Experimental results show the effectiveness of our method. CONCLUSION: Adopting quantitative experimental methods, this work can form more reliable, useful and strong association rules i. e. dominating patterns of amino acid of complex protein misfolded diseases. Thus, in addition to usual applications, the identified patterns can be more useful in discovering medicines for protein misfolded diseases and thereby may open up new opportunities in medical science to handle genetic disorder diseases.

A wildcard character in a pattern adds an additional feature in the field of pattern matching. In this paper, we consider two problems of secure pattern matching (SPM) with wildcards: (i) SPM with repetitive wildcards (SPM-RW) and (ii) SPM with compound wildcards (SPM-CW). Here we consider that a type of wildcard characters “*” is used to represent gaps in the pattern for the first problem of SPM with wildcards. Usually, a wildcard character “*” is used to replace with zero or more letters in the text for the pattern matching problem. Yasuda et al. (ACISP 2014) proposed a protocol with an existing data packing method for secure wildcards pattern matching using symmetric somewhat homomorphic encryption (SwHE) in the semi-honest model in which a wildcard character in the pattern is replaced with just one letter in the text. Furthermore, we enhance their work to replace a wildcard with any sequence of letters in the text then propose SPM-RW protocols by using the symmetric and public-key SwHE schemes in the semi-honest model. Also, we propose a packing method that improves the number of homomorphic multiplications by a factor of k compared to a naive usage of Yasuda et al.’s method to solve the SPM-RW problem in which k is the number of sub-patterns. Next, we consider the SPM-CW problem for processing private database queries, which allows a few types of wildcards (“$”, “*”, and “!”) to appear in the pattern. To solve this problem, we propose an SPM-CW protocol using a double-query technique with public-key SwHE encryption in the semi-honest model. Our experiments exhibit the practicality of the new protocols for SPM-RW and SPM-CW, which outperforms state-of-the-art.

For conventional secret sharing, if cheaters can submit possibly forged shares after observing shares of the honest users in the reconstruction phase, they can disturb the protocol and only they can reconstruct the true secret. To overcome the problem, secret sharing scheme with properties of cheater-identification have been proposed. Existing protocols for cheater-identifiable secret sharing assumed non-rushing cheaters or honest majority. In this paper, using message authentication, we remove both conditions simultaneously, and give its universal construction from any secret sharing scheme. To resolve this end, we explicitly propose the concepts of “individual identification” and “agreed identification”. For both settings, we provide protocols for cheater-identifiable secret sharing. In our protocols, the security parameter can be set independently of the share size and the underlying finite field size.

Multiple group setting schemes have recently become important for enabling deduplication for cloud servers. We consider a new primitive, cross-group deduplication, allowing the multiple groups by the group signature features. We propose a new framework DDUP-MUG (deduplication for the multiple-group signature scheme) that allows one or more groups to access a file such that the cloud storage server can avoid duplicates according to the ownership of the file. The main goal of the primitive is allowing to multiple groups with individual management and several clients from different groups who attempt to store an identical message on the server. In this paper, the group managers mainly manage the new entities and produce revocation lists for clients and the server respectively. We use Message Lock Encryption (MLE) as an ingredient for deduplication and we provide new three protocols, namely UPL-Dup (for uploading a new message), EDT-Dup (for editing the existing message) and DEL-Dup (for eliminating the existing message) in the DDUP-MUG framework.

In PKC 2014, Langlois et al. proposed the first lattice-based group signature scheme with the verifier-local revocability. The security of their scheme is selfless anonymity, which is weaker than the security model defined by Bellare, Micciancio and Warinschi (EUROCRYPT 2003). By using the technique in the group signature scheme proposed by Ling et al. (PKC 2015), we propose a group signature scheme with the verifier-local revocability. For the security discussion of our scheme, we adapt the BMW03 model to cope with revocation queries, since the BMW03 model is for static groups. Then, we show that our scheme achieves the full anonymity in the adapted BMW03 model.

Short Message Service (SMS) is one of the most popular services in the Global System for Mobile (GSM) and many challenges for security arise from the development of message transmission among the broadband network. Recently, an interesting technique called signcryption has been proposed, in which both the properties of signature (ownership) and encryption are simultaneously implemented, with better performance than the traditional signature-then-encryption approach. For the secure mobile computing, we propose a new method for two groups of users that send encrypted signed data by using SMS. We propose a new primitive called verifiable hash convergent group signcryption (VHCGS) by adding the properties of group signcryption and the verification facilities for the third party called the service provider. This research targets toward the type of applications in mobile computing and communication device concerning about SMS.

Somewhat homomorphic encryption is public key encryption supporting a limited number of additions and multiplications on encrypted data. This encryption gives a powerful tool in performing meaningful computations with protecting data confidentiality, whose property is suitable mainly in cloud computing. In this paper, we focus on the scheme proposed by Brakerski and Vaikuntanathan, and present two types of packed ciphertexts in order to improve performance and reduce size of the encrypted data. One type of our packed ciphertexts is based on the message encoding technique proposed by Lauter, Naehrig and Vaikuntanathan. While their technique empowers efficient secure computation of sums and products over the integers, our second type of packed ciphertexts enables efficient secure computation of more complex functionalities such as multiple inner products and multiple Hamming distances. We apply our packing method to construct several protocols for secure biometric authentication and secure pattern matching computations. Our implementation shows that our method gives faster performance than the state-of-the-art work in such applications. Copyright © 2015 John Wiley & Sons, Ltd.

Consider the scenario where we are given an ideal functionality of oblivious transfer (OT), and we wish to construct a larger OT by invoking the above functionality as a black box. How many invocations of an ideal OT functionality are necessary? In tackling this problem, some lower bounds were derived using entropy previously. In this paper, we manage to achieve tighter lower bounds by employing a combinatorial approach. This new approach yields lower bounds which are two times larger than the existing bounds.

The D-ABDUCTOR system was originally developed as a diagram-based idea organizer with facilities for visualization and manipulation of a large class of graphs. It has since been enhanced to improve its extensibility, and now function exchangeability, functional independence, and configurability without programming have become important features. The enhanced system and its features are illustrated with several examples of its application.

We study slender context-sensitive languages, i.e., those containing at most a constant number of words of each length. Recently, it was proved that every slender regular language can be described by a finite union of terms of the form uv i w [9] and every slender context-free language can be described by a finite union of terms of the form uv i wx i y [4, 10]. We show a hierarchy of slender languages which is properly contained in the family of context-sensitive languages and which starts with the family of slender context-free languages, or slender regular languages. Each slender context-sensitive language in the hierarchy can be described by a finite union of terms of the form x 1 y i 1 x 2 y i 2 ··· x n y i n x n+1 .

The rapid growth of data in large databases, such as text databases and scientific databases, requires efficient computer methods for automating analyses of the data with the goal of acquiring knowledges or making discoveries. Because the analyses of data are generally so expensive, most parts in databases remains as raw, unanalyzed primary data. Technology from machine learning (ML) will offer efficient tools for the intelligent analyses of the data using generalization ability. Generalization is an important ability specific to inductive learning that will predict unseen data with high accuracy based on learned concepts from training examples. In this article, we apply ML to text‐database analyses and knowledge acquisitions from text databases. We propose a completely new approach to the problem of text classification and extracting keywords by using ML techniques. We introduce a class of representations for classifying text data based on decision trees; (i.e., decision trees over attributes on strings) and present an algorithm for learning them inductively. Our algorithm has the following features: It does not need any natural language processing technique and it is robust for noisy data. We show that our learning algorithm can be used for automatic extraction of keywords for text retrieval and automatic text categorization. We also demonstrate some experimental results using our algorithm on the problem of classifying bibliographic data and extracting keywords in order to show the effectiveness of our approach.