Cloud computing offers various services based on outsourced data by utilizing its huge volume of resources and great computation capability. However, it also makes users lose full control over their data. To avoid the leakage of user data privacy, encrypted data are preferred to be uploaded and stored in the cloud, which unfortunately complicates data analysis and access control. In particular, few existing works consider the fine-grained access control over the computational results from ciphertexts. Though our previous work proposed a framework to support several basic computations (such as addition, multiplication and comparison [1]) with flexible access control, privacy-preserving division calculations over encrypted data, as a crucial operation in many statistical processes and machine learning algorithms, is neglected. In this paper, we propose four privacy-preserving division computation schemes with flexible access control to fill this gap, which can adapt to various application scenarios. Furthermore, we extend a division scheme over encrypted integers to support privacy-preserving division over multiple data types including fixed-point numbers and fractional numbers. Finally, we give their security proof and show their efficiency and superiority through comprehensive simulations and comparisons with existing work.
Internet of Things (IoT), as a typical representation of cyberization, enables the interconnection of physical things and the Internet, which provides intelligent and advanced services for industrial production and human lives. However, it also brings new challenges to IoT applications due to heterogeneity, complexity and dynamic nature of IoT. Especially, it is difficult to determine the sources of specified data, which is vulnerable to inserted attacks raised by different parties during data transmission and processing. In order to solve these issues, data provenance is introduced, which records data origins and the history of data generation and processing, thus possible to track the sources and reasons of any problems. Though some related researches have been proposed, the literature still lacks a comprehensive survey on data provenance in IoT. In this paper, we first propose a number of design requirements of data provenance in IoT by analyzing the features of IoT data and applications. Then, we provide a deepinsight review on existing schemes of IoT data provenance and employ the requirements to discuss their pros and cons. Finally, we summarize a number of open issues to direct future research.
Probiotics are known to contribute to the anti-oxidation, immunoregulation, and aging delay. Here, we investigated the extension of lifespan by fermented pickles-origin Pediococcus acidilactici (PA) in Caenorhabditis elegans (C. elegans), and found that PA promoted a significantly extended longevity of wild-type C. elegans. The further results revealed that PA regulated the longevity via promoting the insulin/IGF-1 signaling, JNK/MAPK signaling but not TOR signaling in C. elegans, and that PA reduced the reactive oxygen species (ROS) levels and modulated expression of genes involved in fatty acids uptake and lipolysis, thus reducing the fat accumulation in C. elegans. Moreover, this study identified the nrfl-1 as the key regulator of the PA-mediated longevity, and the nrfl-1/daf-18 signaling might be activated. Further, we highlighted the roles of one chloride ion exchanger gene sulp-6 in the survival of C. elegans and other two chloride ion channel genes clh-1 and clh-4 in the prolonged lifespan by PA-feeding through the modulating expression of genes involved in inflammation. Therefore, these findings reveal the detailed and novel molecular mechanisms on the longevity of C. elegans promoted by PA.
Emerging technology developments and functions of the Internet of Things (IoT) in industrial systems are leading the development of the Industrial IoT (IIoT). Greener, i.e., cleaner environmental goals can be achieved by putting green IIoT (GIIoT) into practice. This research aims to explore the reasons for the adoption of GIIoT in organizational decision-making and to explore its impact on organizational performance. The proposed research model was tested by collecting data through a structured questionnaire. The findings suggest that institutional isomorphism has a positive impact on the adoption of GIIoT. Moreover, GIIoT is positively associated with green innovation (GI) practices (e.g., product, process, and management) that lead to organizational performance. The potential impact of various types of institutional isomorphism described in this study can help organizations better comprehend the institutional pressures they enforce and/or appease their stakeholders, especially as they adopt GIIoT, to manage production issues and potential compliance pressures in the process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.