Abdelfattah Essarrar · 2017
As Cloud Computing appeared, it started influencing not only the IT industry, but also all other sectors. Because of its ocular advantages in cost reducing and smooth delivery of services, its use increased exponentially as almost all organizations are tending to outsource their data into the Cloud to benefit from these advantages. But data privacy concerns may make organizations rethink about which type of Cloud will suit their data privacy policy, and which “trusted” Cloud Provider to whom they are going to confide their own data. Nevertheless, there is a wide range of regulations and standards, that rule outsourcing data in Cloud to enforce data privacy. From the technical point of view, there are also multiple techniques that Cloud Providers are implementing in their Cloud Offerings, and other techniques that the Cloud user may adopt to address data privacy concerns. Thus, it is a matter of better matching the user’s data privacy requirements with the adequate solution.
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БРИЖКО, В.М. · The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2016
Cited by 0Open access
Disha Garg · 2021
In today’s rapidly evolving era of medical science, the privacy and security of electronic health records are given the most importance since digital data, which are preferably stored in a cloud environment, attract various hackers and intruders. In the healthcare sector, data analytics and market research are used by insurance agencies to study the pattern of claims submitted by customers or to study demographics of people to acquire new and more customers. Since healthcare data are highly confidential and vast in nature, users often store them in a cloud in encrypted form. The data privacy laws are essential in seeking answers to questions such as: Who owns the complete picture of the user? What risks are associated with situations of massive data leaks? To date, the privacy issues still exist since no stringent controls are in place to protect users’ sensitive data, especially in the healthcare sector, and privacy continues to be an important domain for discussion and further research.
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Masarath Begum, Mohammed Abdul Waheed · International Journal of Engineering and Advanced Technology · 2020
Cloud is now widely used for the remote storage of data; it’s an On-demand device and computer resource configuration process. This allows users to avoid locally saving and storing data. Remote data sharing is an inexpensive and effective way to share cloud users community resources. DiffieHellman used the previous approach to protect multi-owner cloud sharing for distributed groups. In the existing system, there is a community signature shared among all group members that contributes to the middle attack. The program suggested using the LFSR-dependent correlation method, which primarily used handshake protocol to safely exchange community signature to detect the attack, to detect an attack. If the calculated value exceeds one (value>1), the community's public key is changed to avoid abuse.
Cited by 1Open access
V. BRYZHKO · INFORMATION AND LAW · 2016
On the privacy and personal data protection in terms of modern technologies development. Suggestions are provided in relation to introduction of institute of right of private ownership of a person on the personal information in Ukraine.
Cited by 1Open access
Bao, Wei · Journal of the Arkansas Academy of Science · 2021
Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected. Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics. The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems. To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users. To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user's data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee. The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee. In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures.
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Niharika Singh, Ashutosh Kumar Singh · Data Science and Engineering · 2017
Cited by 54Open access
Tom Kirkham, Django Armstrong, Karim Djemame et al. · Research at the University of Copenhagen (University of Copenhagen) · 2012
Cloud transformations require dynamic redistribution of resources across cloud infrastructure. From a legal perspective this movement of data from one data processor to another without the explicit consent of the data subject is a threat to data privacy. Levels of assurance and accountability have to be provided from the cloud infrastructure providers to the data subject in order to maintain trust. In cases of Cloud Transformation multiple providers are present and passing accountability down the chain is essential. Existing Service Level Agreements (SLA) and policy based privacy implementations fail to provide the flexibility and accountability needed in establishing these new relationships. By introducing combined risk and privacy assessment alongside SLA negotiation, the legal and data management implications of Cloud Transformation events can be better accounted for. This will better protect the privacy of data subjects and increase confidence and trust in the Cloud computing platform.
Cited by 17Open access
S. Srinivasan · International Journal for Digital Society · 2016
Cloud Computing is widely used globally today. It has many benefits to offer. However, two of the major concerns for cloud users has been the issue of data security and privacy for data stored in the cloud. Centralized data attracts the attention of government regulators and hackers because of the availability of information in one source. One of the benefits of cloud use is the ability to modify business processes based on data. The main thrust of this paper is to show that when business processes involve the use of cloud then there is the potential for loss of privacy but other resources available in the cloud should help with data protection. We will address this aspect extensively in this paper and suggest some solutions to preserve privacy of individuals and organizations. One aspect of this involves the use of Big Data in the cloud. We will point out that the benefits of using the cloud for large volume storage and handling multiple sources of data. One of the issues with combining data from multiple sources is the ability to re-identify individuals from anonymous data. We point out how privacy is significantly affected when reidentification occurs.
Cited by 1Open access
M. P. Karthikeyan · 2019
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Henry Chima Ukwuoma, Arome Junior Gabriel, Aderonke Favour-Bethy Thompson et al. · 2021
Data security in the cloud has a been major issue upon the inception of cloud computing, frameworks have been proposed and implemented yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies that there is an urgent need to come up with an improved system that will provide adequate data security for both cloud and quantum computing. However, some cryptosystems will be rendered susceptible and obsolete although, some cryptosystems will stand the test of quantum computing. This paper proposes an optimised system for cloud data security using McEliece and NTRU cryptosystems for data security in cloud and quantum computing. While a of McEliece cryptosystem will be used for securing user credentials, a variant of NTRU will also be used for securing user data.
Cited by 2
Somayeh Sobati Moghadam · 2017
Actuellement, les scenarios d’externalisation de donnees deviennent de plus en plus courants avec l’avenement de l’infonuagique. L’infonuagique attire les entreprises et les organisations en raison d’une grande variete d’avantages fonctionnels et economiques.De plus, l’infonuagique offre une haute disponibilite, le passage d’echelle et une reprise apres panne efficace. L’un des services plus notables est la base de donnees en tant que service (Database-as-a-Service), ou les particuliers et les organisations externalisent les donnees, le stockage et la gestion `a un fournisseur de services. Ces services permettent de stocker un entrepot de donnees chez un fournisseur distant et d’executer des analysesen ligne (OLAP).Bien que l’infonuagique offre de nombreux avantages, elle induit aussi des problemes de s´securite et de confidentialite. La solution usuelle pour garantir la confidentialite des donnees consiste a chiffrer les donnees localement avant de les envoyer a un serveur externe. Les systemes de gestion de base de donnees securises utilisent diverses methodes de cryptage, mais ils induisent un surcout considerable de calcul et de stockage ou revelent des informations sur les donnees.Dans cette these, nous proposons une nouvelle methode de chiffrement (S4) inspiree du partage secret de Shamir. S4 est un systeme homomorphique additif : des additions peuvent etre directement calculees sur les donnees cryptees. S4 trait les points faibles des systemes existants en reduisant les couts tout en maintenant un niveau raisonnable de confidentialite. S4 est efficace en termes de stockage et de calcul, ce qui est adequat pour les scenarios d’externalisation de donnees qui considerent que l’utilisateur dispose de ressources de calcul et de stockage limitees. Nos resultats experimentaux confirment l’efficacite de S4 en termes de surcout de calcul et de stockage par rapport aux solutions existantes.Nous proposons egalement de nouveaux schemas d’indexation qui preservent l’ordre des donnees, OPI et waOPI. Nous nous concentrons sur le probleme de l’execution des requetes exacts et d’intervalle sur des donnees chiffrees. Contrairement aux solutions existantes, nos systemes empechent toute analyse statistique par un adversaire. Tout en assurant la confidentialite des donnees, les schemas proposes presentent de bonnes performances et entrainent un changement minimal dans les logiciels existants.
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Sobati Moghadam, Somayeh · theses.fr (ABES) · 2017
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Waleed Almuseelem, Chun Lin Li · Advanced materials research · 2014
Cloud computing has led to the development of IT to more sophisticated levels by improving the capacity and flexibility of data storage and by providing a scalable computation and processing power which matches the dynamic data requirements. Cloud computing has many benefits which has led to the transfer of many enterprise applications and data to public and hybrid clouds. However, many organizations refer to the protection of privacy and the security of data as the major issues which prevent them from adopting cloud computing. The only way successful implementation of clouds can be achieved is through effective enhancement and management of data security and privacy in clouds. This research paper analyzes the privacy and protection of data in cloud computing through all data lifecycle stages providing an overall perspective of cloud computing while highlighting key security issues and concerns which should be addressed. It also discusses several current solutions and further proposes more solutions which can enhance the privacy and security of data in clouds. Finally, the research paper describes future research work on the protection of data privacy and security in clouds.
Cited by 4
Theo Lynn, John G. Mooney, Lisa van der Werff et al. · Palgrave studies in digital business & enabling technologies · 2020
This multi-disciplinary series will provide a comprehensive and coherent account of cloud computing, social media, mobile, big data, and other enabling technologies that are transforming how society operates and how people interact with each other. Each publication in the series will focus on a discrete but critical topic within business and computer science, covering existing research alongside cutting edge ideas. Volumes will be written by field experts on topics such as cloud migration, measuring the business value of the cloud, trust and data protection, fintech, and the Internet of Things. Each book has global reach and is relevant to faculty, researchers and students in digital business and computer science with an interest in the decisions and enabling technologies shaping society.
Cited by 20Open access
Modak, Shreya · TRAP@NCI (National College of Ireland) · 2023
Diabetes is a common chronic illness that has a significant global impact on public health. Timely intervention and management of diabetes depend on the early and precise classification of diabetes status, whether the patient is healthy, pre-diabetic, or diabetic. Using the CDC Diabetes Health Indicators dataset, CDC Diabetes Health Indicators (2015) this research explores the field of machine learning to create a reliable diabetes categorization system. For a sensitive data, such as patients’ health records data, mitigating the data security is a major issue and on the other hand it needs to be served to the doctors and the patients. Encryption is a technique in the clouds to train machine learning models which in this case is solved as AWS sage maker offers capabilities such as industry-standard SSL/TLS protocols encryption that handles mitigating the security risks for a secure model training and deployment in the clouds Theerthagiri et al. (2022). Thus, this study investigates the performance of the distributed computing specifically parallel processing, DE duplication and compares the efficiency of various machine learning algorithms; Random “Forest, xGBoost and Tensor Flow” featuring federated learning techniques and data anonymization techniques. The dataset adopted for this study is the Centres for Disease Control and Prevention (CDC) which funds the CDC Diabetes Health Indicators dataset, which is a useful resource containing 253,680 occurrences and 21 attributes that include health and lifestyle data as well as demographics. This dataset provides a thorough understanding of the intricate connection between diabetes and lifestyle variables that is our centre goal of this study. The three machine learning algorithms that are compared in this study come with special advantages for each for categorising features according to the characteristics in the dataset. Using feature significance analysis and several decision trees, Random Forest is an ensemble approach. Neural network architecture is used by Tensor Flow, which is inspired by the structure of the human brain, to recognise complex patterns. The goal of xGBoost algorithm is to identify the best hyper planes for data separation. The aim of the study is to identify which algorithm provides the most trustworthy and accurate diabetes status categorization in the clouds. The assessment will take into account indicators like F1-score, recall, accuracy, and precision. This study advances healthcare analytics by identifying the most efficient method, which may improve early diabetes identification and individualised treatment recommendations.
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T. Subha, S. Jayashri · i-manager’s Journal on Cloud Computing · 2014
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Satyavathi Divadari, J. Surya Prasad, Prasad B. Honnavalli · Lecture notes in networks and systems · 2023
Cited by 2
Pierangela Samarati · Lecture notes in computer science · 2014
Cited by 26
Xueli Huang · TUScholarShare (Temple University) · 2016
The growing concerns in term of the privacy of data stored in public cloud have restrained the widespread adoption of cloud computing. The traditional method to protect the data privacy is to encrypt data before they are sent to public cloud, but heavy computation is always introduced by this approach, especially for the image and video data, which has much more amount of data than text data. Another way is to take advantage of hybrid cloud by separating the sensitive data from non-sensitive data and storing them in trusted private cloud and un-trusted public cloud respectively. But if we adopt the method directly, all the images and videos containing sensitive data have to be stored in private cloud, which makes this method meaningless. Moreover, the emergence of the Software-Defined Networking (SDN) paradigm, which decouples the control logic from the closed and proprietary implementations of traditional network devices, enables researchers and practitioners to design new innovative network functions and protocols in a much easier, flexible, and more powerful way. The data plane will ask the control plane to update flow rules when the data plane gets new network packets with which it does not know how to deal with, and the control plane will then dynamically deploy and configure flow rules according to the data plane's requests, which makes the whole network could be managed and controlled efficiently. However, this kind of reactive control model could be used by hackers launching Distributed Denial-of-Service (DDoS) attacks by sending large amount of new requests from the data plane to the control plane. For image data, we divide the image is into pieces with equal size to speed up the encryption process, and propose two kinds of method to cut the relationship between the edges. One is to add random noise in each piece, the other is to design a one-to-one mapping function for each piece to map different pixel value into different another one, which cuts off the relationship between pixels as well the edges. Our mapping function is given with a random parameter as inputs to make each piece could randomly choose different mapping. Finally, we shuffle the pieces with another random parameter, which makes the problems recovering the shuffled image to be NP-complete. For video data, we propose two different methods separately for intra frame, I-frame, and inter frame, P-frame, based on their different characteristic. A hybrid selective video encryption scheme for H.264/AVC based on Advanced Encryption Standard (AES) and video data themselves is proposed for I-frame. For each P-slice of P-frame, we only abstract small part of them in private cloud based on the characteristic of intra prediction mode, which efficiently prevents P-frame being decoded. For cloud running with SDN, we propose a framework to keep the controller away from DDoS attack. We first predict the amount of new requests for each switch periodically based on its previous information, and the new requests will be sent to controller if the predicted total amount of new requests is less than the threshold. Otherwise these requests will be directed to the security gate way to check if there is a attack among them. The requests that caused the dramatic decrease of entropy will be filter out by our algorithm, and the rules of these request will be made and sent to controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to honey pot.
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