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Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.

A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.

Cache memory plays an important role in improving the performance of web servers, especially for big data transmission, which response time is constrained. It is necessary to use an effective method, such as web cache. Because an outstanding cache replacement algorithm can not only reduce the users access time but also improve the performance of the system. The traditional used weighting replacement policy does not consider the size parameter, hence, it may perform poorly while the datasets are larger. In this paper, we propose a novel, high-performance cache replacement algorithm for the web cache, named weighting size and cost replacement policy (WSCRP) bases on the weighting replacement policy. The algorithm recalculates the objects weight with adding the cost attribute in the cache, then orders the weight. Additionally the influence of various factors on the Web object as frequency, time, and cost value are considered. When the cache space cannot satisfy the new request object, the replacement policy WSCRP replaces the largest weighting and cost object. The experiments show that proposed algorithm has higher

Caches are used to improve the performance of the internet, and to reduce the latency of data access time and the low speed of repeated computing processes. Cache replacement is one of the most important issues in a caching system; therefore, it must be coordinated with the caching system to minimize the access latency and maximize the hit rate or byte hit rate. In this paper, we presented a novel caching replacement algorithm named Weighted Greedy Dual Size Frequency (WGDSF) algorithm, which is an improvement on the Greedy Dual Size Frequency (GDSF) algorithm. The WGDSF algorithm mainly adds weighted frequency-based time and weighted document type to GDSF. By increasing the above two weighted parameters, WGDSF performs fairly well at keeping popular objects in the cache and replacing rarely used ones. Our experiment shows that this algorithm has a better hit rate, byte hit rate and access latency than state-of-the-art algorithms, such least Recently Used, least Frequently Used, and GDSF.

With the fast development of social networks, more and more data has been generated. Finding useful information among these data is important. Subgraph matching is the method that can be used in social networks for social search, recommender systems, and so on. In this paper, we choose five typical graph matching methods which are VF2, SPath, TurboISO, BoostIso, and RI to value their performance and scalability in social networks. These methods are verified by three social network data sets whose node’s number vary from thousands to millions. According to the experiments, we can find that VF2 and RI is applicable for rather small graphs while SPath performs better in large graph when average degree of graph is small. When graph with high average degree, TurboISO and BoostIso performs better. What is more, we also compare the efficiency of different matching order when searching. The order choosing strategy proposed by RI is better.

Due to advancements in the development of wireless medical sensing devices and wireless communication technologies, the wireless body area network (WBAN) has become an eminent part of e-healthcare systems. WBAN uses medical sensors to continuously monitor and collect the physiological parameters of a patient's health and send them to a remote medical server through a portable digital assistance (PDA)/mobile. Due to limitations in communication, such as power, storage, and the computational capabilities of sensors, data aggregation techniques are used to reduce the communication overhead in real-time data transmission in WBAN. However, since the WBAN transmits sensitive health data, data security and data privacy are a major concern. In this paper, we propose a secure privacy-preserving data aggregation (SPPDA) scheme based on bilinear pairing for remote health monitoring systems to improve data aggregation efficiency and data privacy. Our proposed SPPDA scheme utilizes the homomorphic property of the bilinear ElGamal cryptosystem to perform privacy-preserving secure computation and combines it with the aggregate signature scheme, enabling data authenticity/integrity in the WBAN. The proposed SPPDA scheme is proved to be semantically secure under the decisional bilinear Diffie-Hellman assumption. Security analysis demonstrates that our proposed scheme preserves data confidentiality, data authenticity, and data privacy; it also resists passive eavesdropping and replay attacks. A performance evaluation based on simulation results and a comparison of computational cost with related schemes show that data aggregation and batch verification at the PDA significantly reduce communication and transmission overhead and support efficient computation at the remote server.

The massive reach of social networks (SNs) has hidden their potential concerns, primarily those related to information privacy. Users increasingly rely on social networks for more than merely interactions and self-representation. However, social networking environments are not free of risks. Users are often threatened by privacy breaches, unauthorized access to personal information, and leakage of sensitive data. In this paper, we propose a privacy-preserving model that sanitizes the collection of user information from a social network utilizing restricted local differential privacy (LDP) to save synthetic copies of collected data. This model further uses reconstructed data to classify user activity and detect abnormal network behavior. Our experimental results demonstrate that the proposed method achieves high data utility on the basis of improved privacy preservation. Moreover, LDP sanitized data are suitable for use in subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method's reconstructed data achieves a detection accuracy similar to that on the original data.

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet’s payload. The scheme uses homomorphic encryption techniques to conceal the report’s measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet’s size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.

Energy efficiency is one of the major issues in wireless sensor networks (WSNs) and their applications. Distributed techniques with low message and time complexities are expected in WSNs. Connected dominating sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. Most of the existing CDS approaches suffer from a very poor approximation ratio, high time, and message complexities. This paper proposes two novel approaches for CDS distributed construction in WSNs. The proposed approaches are intended to construct a small CDS as well as allowing energy-efficient CDS construction and maintenance in WSNs. Simulation shows that our distributed approaches have an approximation factor of 7.5 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms.

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

Wireless sensor networks (WSNs) are spreading rapidly due to their flexibility to communicate which demands a secure environment. The most important requirements of WSN security are confidentiality, authentication, and integrity. User authentication is necessary for legitimate access control in WSNs. Sensors have limited processing power, bandwidth, memory, and limited communication abilities. Significantly, the system must produce an authentication method to confirme if the user is legal or not. In this paper, we present a solution based on biometric and adapt it for a WSN environment. The proposed protocol involves simple operations and light computations. The main advantage of the proposed protocol is using the user's iris to regenerate the user's key on-the-fly every time the user wants be authenticated which dramatically enhances security aspects in WSNs. The key used in this protocol is stronger than passwords and shorter than biometric data, which balances between security and performance. Our protocol uses much light computations and simple operations in both homogenous and heterogeneous environments; therefore, it is suitable to the WSNs.

Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework (TTGCIF) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that none of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in TTGCIF is trained to learn data semantic association among different domains via semantic prototype transduction, so that the "unlabeled" crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in TTGCIF can be sensitive to any alteration on training data, ensuring that even if only few annotated resource in target domain are available for "Fine-tune," TTGCIF can still react promptly to achieve effective target domain adaptation. According to the experimental results, TTGCIF can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on no more than 10% annotated resource in target domain. More importantly, even if TTGCIF has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by TTGCIF on the target domain are still superior to those generated by models already fine-tuned on the target domain. [ABSTRACT FROM AUTHOR]