In the context of healthcare 4.0, cloud‐based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper‐proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy‐preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state‐of‐the‐art ones.

To prevent the abuse of low-rate speech-based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low-bit-rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self-paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under-sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under-sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low-bit-rate speech-based steganography, and the steganalysis scheme based on the self-paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self-paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self-paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.

In practice, we usually require two cryptosystems, an asymmetric one and a symmetric one, to encrypt a large confidential message. The asymmetric cryptosystem is used to deliver secret key SK, while the symmetric cryptosystem is used to encrypt a large confidential message with the secret key SK. In this article, we propose a simple cryptosystem which allows a large confidential message to be encrypted efficiently. Our scheme is based on the Diffie-Hellman distribution scheme, together with the ElGamal cryptosystem.

The spatial relationships among pictorial objects are important spatial characteristics in image database systems. Based on the concept of 9-DLT representation, we propose a new method for effective storage utilization and picture retrieval. First, we transform a symbolic picture into a set of triples associated with pairwise spatial relationships among objects. Then, an associated normalized signature record with nine attributes is specified. Finally, each normalized record of this image database can be well allocated by using our proposed multiple key hashing scheme and can be fast accessed for spatial match retrieval. The performance formula for spatial picture retrieval is presented and the theoretically optimal solutions to the bit lengths of all the attributes are derived in certain case. Also, we give a systematic method to solve the feasible bit lengths for the attributes based on the method proposed by Chang. © 1997 John Wiley & Sons, Inc.

The paper first shows that the bucket allocation problem of an MKH (multiple key hashing) file for partial match retrieval can be reduced to that of a smaller sized subfile, called the remainder of the file. And it is pointed out that the remainder type MKH file is the hardest MKH file for which to design an optimal allocation scheme. The authors then particularly concentrate on the allocation of an important remainder type MKH file; namely, the k-ary MKH file. They present various sufficient conditions on the number of available disks and the number of attributes for a k-ary MKH file to have a perfectly optimal allocation among the disks for partial match queries. Based upon these perfectly optimal allocations, they further present a heuristic method, called the CH (cyclic hashing) method, to produce near optimal allocations for the general k-ary MKH files. Finally, a comparison, by experiment, between the performances of the proposed method and an "ideal" perfectly optimal method, shows that the CH method is indeed satisfactorily good for the general k-ary MKH files.

In this paper, we shall give an ordered minimal perfect hashing scheme for letter oriented keys. We applied our method successfully to four practical key sets like the set of animals' identifiers in English, the set of C's keywords, the set of frequently occurring English words and the set of PASCAL'S reserved words.

In this paper, we first introduced the concept of Cartesian product files. We then derived a formula for random files. A computer simulation experiment was performed to compare these two files. So far as shown by the experimental results, the Cartesian product file concept was indeed a good one. We also showed that the problem of designing an optimal Cartesian product file was partially related to the problem of finding a minimal N-tuple. A method to find minimal N-tuples was presented and its properties were discussed.