There is an obvious need of supporting data for resilience against major failures in many situations the process of storing backup data is also enforced by the law. The organizations must identify the probable consequences that can cause disasters and evaluate their impact. plan (DRP) and data backup which falls within the cost constraints while achieving the target recovery requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). Every organization requires a business continuity plan (BCP) or disaster recovery. Data backup and Disaster Recovery / Business Continuity issues are becoming fundamental in networks since the importance and societal value of digital data is continuously increasing. Today, in every organization are generated in large volume of data in electronic format that required the safety storage services. Extensive security and performance analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks. Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on outsourced data, including: block modification, deletion and append.
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By utilizing the homomorphic token with distributed erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server(s).
#Remote data backup verification#
In order to regain the assurances of cloud data integrity and availability and enforce the quality of cloud storage service for users, we propose a highly efficient and flexible distributed storage verification scheme with two salient features, opposing to its predecessors. While cloud storage relieves users from the burden of local storage management and maintenance, it is also relinquishing users’ ultimate control over the fate of their data, which may put the correctness of outsourced data into risks. Also the necessary server-side resources for providing the service are within a reasonable bound.Īs one of the emerging services in cloud paradigm, cloud storage enables users to remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. The proposed framework does not require any user data to be uploaded to the server for data recovery. In this paper, we propose a novel data recovery service framework on cloud infrastructure, a Parity Cloud Service (PCS) that provides a privacy-protected personal data recovery service. Users are not expected to upload their critical data to the internet backup server until they can fully trust the service provider in terms of the privacy protection. Since the privacy protection is a crucial issue for providing a personal data recovery service, a plain data backup-based recovery service is not adequate for public service. While lots of effective backup and recovery technologies, including data dedeplication and incremental backup, have been developed for enterprise level data backup service, few works have been done for efficient personal data recovery service. Potential research directions are pointed out for further optimization of data quality and reliability in Body Area Network (BAN) on the sensor level, network level, and human-centric level.Īs more and more data are generated in an electronic format, the necessity of data recovery service became larger and the development of more efficient data backup and recovery technology has been an important issue during the past decade. The framework is composed of a set of DQ dimensions to verify that the information gleaned from sensors, processed, and delivered are of high quality so that diabetes patients and healthcare professionals are able to make reliable, high-precision diagnoses, and real-time treatment decisions. Based on existing literature on WBAN systems, sensor technologies and data quality (DQ) dimensions, a framework is proposed to ensure high data quality and reliability in WBANs for effective and real-time diabetes monitoring. High data reliability and quality is of paramount importance in WBAN to ensure wide systems’ adoption and technological acceptance. Low quality data can be misleading and thus result in inaccurate diagnosis, ineffective health decision-making, and even loss of lives.
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In wireless body area network (WBAN), data captured from different types of wearable, invasive, minimally invasive, or non-invasive sensors have the immense potential to contribute for real-time decisions and effective healthcare services for better diabetes monitoring.