SEQUENTIAL ENCRYPTION FOR MULTIPLE CHUNKS OF DATA IN CLOUD ENVIRONMENT

Cloud computing is a next generation computer paradigm for IT firm. The vital service of cloud computing is cloud storage, that permits owner to move data from their native computing systems to the cloud. Storing our confidential data to a public cloud is a challenging issue in cloud computing because of unauthorized access to the data. The proposed algorithm splits data and encrypts it using two different algorithms to improve the security level of the confidential data than the various techniques of existing encryption algorithms. The data is first partitioned into multiple chunks and then efficient encryption algorithms such as RSA algorithm and Blowfish algorithm is used for data encryption. It further proposes an efficient data access using indexing technique to retrieve the confidential data from cloud. Finally, it needs to decrypt multiple chunks to get actual data from public cloud. The objectives of the proposed techniques are to store confidential data in public cloud and ensure more security than the existing techniques

Enhancing data security in cloud using random pattern fragmentation and a distributed nosql database

© 2019 IEEE. The cloud computing model has become very popular among users, as it has proven to be a cost-effective solution to store and process data, thanks to recent advancements in virtualization and distributed computing. Nevertheless, in the cloud environment, the user entrusts the safekeeping of its data entirely to the provider, which introduces the problem of how secure such data is and whether its integrity has been maintained. This paper proposes an approach to the data security in cloud by utilizing a random pattern fragmentation algorithm and combining it with a distributed NoSQL database. This not only increases the security of the data by storing it in different nodes and scramble all the bytes, but also allows the user to implement an alternative method of securing data. The performance of the approach is compared to other approaches, along with AES 256 encryption. Results indicate a significant performance improvement over encryption, highlighting the capabilities of this method for cloud stored data, as it creates a layer of protection without additional overhead

Statistical Study of Supply Chain Developmental Training on Original Equipment Manufacturer’s Defect Rates

Learning is an amalgam of a student’s desire to understand, the willingness of an instructor to educate, the subject taught, its quality, and the delivery environment. Most importantly, a learning goal, both actionable and investment worthy, must exist for effective learning to take place. The ability to learn and then implement new concepts and ideas is the significant difference separating our species from other life on this planet. It has led to diverse discoveries such as disease vaccines, nuclear energy, the automobile, electronics, and rocket planes to name just a few.Science advances in small steps and big leaps. Inventions increase daily to improve the quality of life for this planet’s population. At the center of all these ideas are researchers looking to tease out the next piece of information for our world’s knowledge base. This body of knowledge (BOK) grows at an ever-expanding rate, doubling every few years. This doubling period is shrinking rapidly as more knowledge is accumulated.In the manufacturing world of products, this BOK is brought to bear on products that, hopefully, stand above their competition. If a product is excellent, consumers and producers are satisfied. The consumer gets the best quality for the price while the producer gets the best price for the quality offered. If product quality and price are right, there is someone willing to buy it.However, what happens when the opposite is experienced and the product, at its asking price, is of poor quality? Buyers are less willing to spend and quick to mention the lack of quality or defects found. Defects can impact the maker’s selling price and translate into extensive efforts to make the customer whole through warranty. The producer also risks the loss of their customer (brand loyalty) if the product is deficient in quality.Within this dissertation, training methods, useful practices, experiences, and the body of training knowledge will be presented in defense of developmental training and will conclude with a case study exploration into the connections between supply chain developmental training and defect reductions at an Original Equipment Manufacturer (OEM) using multiple statistical techniques

Requirements and operational guidelines for secure and sustainable digital phenotyping:Design and development study

Background: Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way.Objective: This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform.Methods: We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform.Results: We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design.Conclusions: Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping–driven research

The Life-Cycle Policy model

Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill-intentioned scrutinization and abusive usages fostered by fuzzy privacy policies. No one is sheltered because a single event (e.g., applying for a job or a credit) can suddenly make our history a precious asset. By definition, access control fails preventing trail disclosures, motivating the integration of the Limited Data Retention principle in legislations protecting data privacy. By this principle, data is withdrawn from a database after a predefined time period. However, this principle is difficult to apply in practice, leading to retain useless sensitive information for years in databases. In this paper, we propose a simple and practical data degradation model where sensitive data undergoes a progressive and irreversible degradation from an accurate state at collection time, to intermediate but still informative degraded states, up to complete disappearance when the data becomes useless. The benefits of data degradation is twofold: (i) by reducing the amount of accurate data, the privacy offence resulting from a trail disclosure is drastically reduced and (ii) degrading the data in line with the application purposes offers a new compromise between privacy preservation and application reach. We introduce in this paper a data degradation model, analyze its impact over core database techniques like storage, indexation and transaction management and propose degradation-aware techniques