Decentralized Inner-Product Encryption with Constant-Size Ciphertext

With the rise of technology in recent years, more people are studying distributed system architecture, such as the e-government system. The advantage of this architecture is that when a single point of failure occurs, it does not cause the system to be invaded by other attackers, making the entire system more secure. On the other hand, inner product encryption (IPE) provides fine-grained access control, and can be used as a fundamental tool to construct other cryptographic primitives. Lots of studies for IPE have been proposed recently. The first and only existing decentralized IPE was proposed by Michalevsky and Joye in 2018. However, some restrictions in their scheme may make it impractical. First, the ciphertext size is linear to the length of the corresponding attribute vector; second, the number of authorities should be the same as the length of predicate vector. To cope with the aforementioned issues, we design the first decentralized IPE with constant-size ciphertext. The security of our scheme is proven under the ℓ-DBDHE assumption in the random oracle model. Compared with Michalevsky and Joye’s work, ours achieves better efficiency in ciphertext length and encryption/decryption cost.

Attribute Propagation Network for Graph Zero-Shot Learning

The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the “attribute propagation network (APNet)”, which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.

Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition

In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset.

Private Evaluation of Decision Trees using Sublinear Cost

Decision trees are widespread machine learning models used for data classification and have many applications in areas such as healthcare, remote diagnostics, spam filtering, etc. In this paper, we address the problem of privately evaluating a decision tree on private data. In this scenario, the server holds a private decision tree model and the client wants to classify its private attribute vector using the server’s private model. The goal is to obtain the classification while preserving the privacy of both – the decision tree and the client input. After the computation, only the classification result is revealed to the client, while nothing is revealed to the server. Many existing protocols require a constant number of rounds. However, some of these protocols perform as many comparisons as there are decision nodes in the entire tree and others transform the whole plaintext decision tree into an oblivious program, resulting in higher communication costs. The main idea of our novel solution is to represent the tree as an array. Then we execute only d – the depth of the tree – comparisons. Each comparison is performed using a small garbled circuit, which output secret-shares of the index of the next node. We get the inputs to the comparison by obliviously indexing the tree and the attribute vector. We implement oblivious array indexing using either garbled circuits, Oblivious Transfer or Oblivious RAM (ORAM). Using ORAM, this results in the first protocol with sub-linear cost in the size of the tree. We implemented and evaluated our solution using the different array indexing procedures mentioned above. As a result, we are not only able to provide the first protocol with sublinear cost for large trees, but also reduce the communication cost for the large real-world data set “Spambase” from 18 MB to 1[triangleright]2 MB and the computation time from 17 seconds to less than 1 second in a LAN setting, compared to the best related work.

Secure Data Sharing in Cloud Using an Efficient Inner-Product Proxy Re-Encryption Scheme

With the ever-growing production of data coming from multiple, scattered, highly dynamical sources (like those found in IoT scenarios), many providers are motivated to upload their data to the cloud servers and share them with other persons with different purposes. However, storing data on cloud imposes serious concerns in terms of data confidentiality and access control. These concerns get more attention when data is required to be shared among multiple users with different access policies. In order to update access policy without making re-encryption, we propose an efficient inner-product proxy re-encryption scheme that provides a proxy server with a transformation key with which a delegator’s ciphertext associated with an attribute vector can be transformed to a new ciphertext associated with delegatee’s attribute vector set. Our proposed policy updating scheme enables the delegatee to decrypt the shared data with its own key without requesting a new decryption key. We experimentally analyze the efficiency of our scheme and show that our scheme is adaptive attribute-secure against chosen-plaintext under standard Decisional Linear (D-Linear) assumption.

A Methodology of Evaluating Service Value based on the Service Field Concept and Its Application to Evaluation of Attractiveness in Sightseeing

Much research has been carried out on evaluating service quality. But, there have been no previous methodology which can measure and evaluate service value mathematically. In this paper the authors discuss the concept of a service field and its application to evaluate service value, which is analogous to the electro-magnetic field in physics. For evaluating service value numerically, service value is defined by an inner product of a provider's service attribute vector and a user's requirement attribute vector. In order to demonstrate the effectiveness, the proposed method is then applied to the evaluation of attractiveness in sightseeing. This mathematical model seems to be useful in evaluating the effectiveness of service theoretically.

New Approach of Features Extraction for Numeral Recognition

This paper proposes a new approach of features extraction based on structural and statistical techniques for handwritten, printed and isolated numeral recognition. The structural technique is inspired from the Freeman code, it consists first of contour detection and closing it by morphological operators. After that, the Freeman code was applied by extending its directions to 24-connectivity instead of 8-connectivity. Then, this technique is combined with the statistical method profile projection to determine the attribute vector of the particular numeral. Numeral recognition is carried out in this work through k-nearest neighbors and fuzzy min-max classification. The recognition rate obtained by the proposed system is improved indicating that the numeral extracted features contain more details.

Partition of a Binary Matrix intok(k ≥ 3) Exclusive Row and Column Submatrices Is Difficult

A biclustering problem consists of objects and an attribute vector for each object. Biclustering aims at finding a bicluster—a subset of objects that exhibit similar behavior across a subset of attributes, or vice versa. Biclustering in matrices with binary entries (“0”/“1”) can be simplified into the problem of finding submatrices with entries of “1.” In this paper, we consider a variant of the biclustering problem: thek-submatrix partition of binary matrices problem. The input of the problem contains ann×mmatrix with entries (“0”/“1”) and a constant positive integerk. Thek-submatrix partition of binary matrices problem is to find exactlyksubmatrices with entries of “1” such that theseksubmatrices are pairwise row and column exclusive and each row (column) in the matrix occurs in exactly one of theksubmatrices. We discuss the complexity of thek-submatrix partition of binary matrices problem and show that the problem is NP-hard for anyk≥3by reduction from a biclustering problem in bipartite graphs.

Content-Based Image Classification and Retrieval

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image classification and retrieval is one of the most important services that must be supported by such systems. The most common approach used is content-based image retrieval (CBIR) systems. This paper presents a new application of rough sets to feature reduction, classification, and retrieval for image databases in the framework of content-based image retrieval systems. The suggested approach combines image texture features with color features to form a powerful discriminating feature vector for each image. Texture features are extracted, represented, and normalized in an attribute vector, followed by a generation of rough set dependency rules from the real value attribute vector. The rough set reduction technique is applied to find all reducts with the minimal subset of attributes associated with a class label for classification.