#Vallum security services corporation software

In this paper, information quality is captured by two indices of a set of PV-related data, i.e., Shannon entropy and non-noise ratio. Specifically, we examine how additional meteorological and PV power data help to improve day-ahead forecasting accuracy, thus enhancing unit commitment (UC). Here we analyze the information value of photovoltaic (PV)-related data in a power system operation problem. To this end, this paper designs an information market framework and proposes a generalized information valuation model to help price data in smart grids efficiently. However, the existing studies and practices pay little attention to information value evaluation, i.e., the potential for information to be converted into actual economic benefits. With the development of internet technology, information has become an increasingly important asset for the operation and planning of power systems. Thus, our solution proved to be feasible and can be expanded and refined for practical use and integration with cloud synchronization services. Our results show an overall performance better than the original Cryptomator application, with stronger security premises. To validate our proposal, we build a proof of concept based on the Cryptomator application, an open-source client-side encryption tool specially designed for cloud storage services.

#Vallum security services corporation software

Aiming to improve the security of the client-side encryption approach, we propose to apply the Intel Software Guard Extensions (SGX) to perform data sealing, creating a secure vault that can be synchronized with any cloud storage service, while relying on the SGX to protect the key handling. This scheme is already explored by many applications on personal computers and also as a native feature in some smartphone operating systems, but are still susceptible to certain types of attacks. An approach to ensure data confidentiality is the use of client-side encryption, with the user taking control of the encryption keys and defining which files or data will be encrypted. Despite the benefits and facilities, it is necessary to pay extra attention to data confidentiality and users’ privacy, as numerous threats aim to collect such information in an unauthorized manner. Nowadays, users are delegating the data storage to cloud services, due to the virtually unlimited storage, change history, broadband connection, and high availability. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. Results on the processing of data in different scenarios are presented and discussed. Data is anonymised inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. In this paper we present an architecture that addresses this problem. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centres. Machine learning strongly benefits from computing accelerators. Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features.