Abstract
Data Migration is a multi-step process that begins with analyzing old data and culminates in data uploading and reconciliation in new applications. With the rapid growth of data, organizations constantly need to migrate data. Data migration can be a complex process as testing must be done to ensure data quality. Migration also can be very costly if best practices are not followed and hidden costs are not identified in the early stage. On the other hand, many organizations today instead of buying IT equipment (hardware and/or software) and managing it themselves, they prefer to buy services from IT service providers. The number of service providers is increasing dramatically and the cloud is becoming the preferred tool for more cloud storage services. However, as more information and personal data are transferred to the cloud, to social media sites, DropBox, Baidu WangPan, etc., data security and privacy issues are questioned. So, academia and industry circles strive to find an effective way to secure data migration in the cloud. Various resolving methods and encryption techniques have been implemented. In this work, we will try to cover many important points in data migration as Strategy, Challenges, Need, methodology, Categories, Risks, and Uses with Cloud computing. Finally, we discuss data migration security and privacy challenge and how to solve this problem by making improvements in it’s using with Cloud through suggested proposed model that enhances data security and privacy by gathering Advanced Encryption Standard-256 (ATS256), Data Dispersion Algorithms and Secure Hash Algorithm-512. This model achieves verifiable security ratings and fast execution times.
Highlights
IntroductionTools or their interpretation under various semantics. This requires a thorough understanding of ancient data sources from various aspects, such as explicit or implicit data constraints, interrelationships across different data sources, and data availability
Data Migration is a multi-step process that begins with analyzing old data and culminates in data uploading and reconciliation in new applications
As more information and personal data are transferred to the cloud, to social media sites, DropBox, Baidu WangPan, etc., data security and privacy issues are questioned
Summary
Tools or their interpretation under various semantics. This requires a thorough understanding of ancient data sources from various aspects, such as explicit or implicit data constraints, interrelationships across different data sources, and data availability. A previous study [6] showed that 62% of data migration projects have significant data quality problems in new systems. Various data migration tasks such as data identification, validation, and cleansing must be performed frequently in the project and specification changes occur frequently to fix the detected problems. It is estimated [6] that 90% of the initial specifications change and over 25% of the specifications change more than once during the life of a data migration project. We try to introduce various aspects of data migration to be clear for reader and how we solve data migration security and privacy challenge using suggested model.
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