A Novel Approach for Data Privacy Using Attribute Based Scheme Algorithm for Cloud Computing

Ponte Academic JournalJun 2017, Volume 73, Issue 6 SOA-BASED PROCEDURE WITH DYNAMIC SECURITY FOR ADAPTIVE CLOUD COMPUTING FRAMEWORKAuthor(s): Vasu Yadav Dokku ,V. Krishna ReddyJ. Ponte - Jun 2017 - Volume 73 - Issue 6 doi: 10.21506/j.ponte.2017.6.9 Abstract:Cloud computing is defined as a ondemand delivery of resources and applications whenever user wants and pay according to its usage. In the cloud data outsourcing and providing real-time data privacy are said to be important concepts to share the data to registered users only. Different cloud approaches was introduced to provide privacy for outsourced data, based on their limitations and advantages, a cloud computing adoption framework (CCAF) was introduced to provide dynamic and customized data privacy in the cloud. In this framework, they used business process modeling notation (BPMN) for data usage and data sharing and outsourcing data in distributed cloud computing. To identify general users and intruders using convergent encryption technique it is possible to provide data security in cloud computing. To provide third party service to outsourced business modeling then CCAF framework will fail to provide dynamic authentication in distributed computing. So in this paper, we propose and implement Novel Cross-Realm Privacy (NCRP) approach for collaborating Service-Oriented Architecture to various security states in outsourced data to provide multiparty business services for dynamic authentication. We improve the CCAF framework, by means of changing the process when outsourcing the data. Our experimental results give the implementation of proposed approach with dynamic authentication and describe the improved performance evaluation with the scalability of outsourced data in cloud computing framework. Download full text:Check if you have access through your login credentials or your institution Username Password

Business people and academia are now excited about Big Data and Cloud Computing as the new and most innovative means for enhancing productivity and customer satisfaction. Simultaneously, there are strong concerns about privacy not only among privacy advocates but among consumers in general, and how to strike a right balance is the main theme in every field of science. However, it is quite strange that very little attention has been paid to the concept of confidentiality, which must be the core element of privacy. This paper first tries to analyze the following two dichotomies as a basis for possible policy considerations: (1) privacy approach in the United States versus confidentiality approach in the United Kingdom, though they share the same common law tradition, and (2) clear demarcation between Information Service and Telecommunications in the United States, dating back to the Computer Inquiry in the 1970s. This paper also analyzes the features of the Cloud and discusses the possibility of treating it as a new type of Public Utility, namely Information Utility. This hypothesis should be rejected, because there are crucial differences in market structures, regardless of clear similarities in service features. Instead, this paper emphasizes the necessity of protecting confidentiality as an industrial norm. Taking into account the long tradition of free market for computing industries, self-regulation is basically preferable to government regulation. But from a different viewpoint of nudge, a hybrid combination of libertarianism and paternalism, this paper concludes by proposing five short recommendations including fair contract terms as well as unbundling confidentiality from privacy.

Data privacy law and competition law are frequently perceived as two separate regimes, each pursuing distinct objectives, differing in their material scope, and addressing different types of harm. Thus, the conventional approach has been to treat them separately. However, the increasing commercialization of personal data has sparked a global debate on the need to address the interface between the two legal regimes. This introduction sets the stage for the book’s investigation of this nexus, outlining the key developments that inspired and shaped its journey. The book’s concept originated with Facebook’s acquisition of WhatsApp in 2014 and the European Commission’s merger decision, which epitomized the conventional approach of either data privacy or competition law. This exploration culminates with the 2023 Meta v Bundeskartellamt decision where the Court of Justice of the European Union underscored the vital role of data privacy rules in competition assessment. Adding a new dimension to the interface is the growing tendency of digital incumbents to engage in potentially anticompetitive practices under the guise of enhancing user privacy. Another crucial development is the recognition that the commercialization of personal data creates a complex economic landscape, marked by multiple actors, a web of interactions, and non-linear, emergent outcomes. This awareness enables the examination of the intersection between data privacy and competition law through a fresh analytical lens using insights from complexity science. The use of this analytical framework makes it possible to expose a systemic problem common to both sets of rules and to propose potential solutions.

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

Blockchain-Based Secure Cloud Data Management: A Novel Approach for Data Privacy and Integrity

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

This study aims to evaluate scientific publications on "Medical Informatics" and "Data Privacy" using a bibliometric approach to identify research trends, the most studied topics, and the countries and institutions with the highest publication output. The search was carried out utilizing the WoS Clarivate Analytics tool across SCIE journals. Subsequently, text mining, keyword clustering, and data visualization were applied through the use of VOSviewer and Tableau Desktop software. Between 1975 and 2023, a total of 7,165 articles were published on the topic of data privacy. The number of articles has been increasing each year. The text mining and clustering analysis identified eight main clusters in the literature: (1) Mobile Health/Telemedicine/IOT, (2) Security/Encryption/Authentication, (3) Big Data/AI/Data Science, (4) Anonymization/Digital Phenotyping, (5) Genomics/Biobank, (6) Ethics, (7) Legal Issues, (8) Cloud Computing. On a country basis, the United States was identified as the most active country in this field, producing the most publications and receiving the highest number of citations. China, the United Kingdom, Canada, and Australia also emerged as significant countries. Among these clusters, "Mobile Health/Telemedicine/IOT," "Security/Encryption/Authentication," and "Cloud Computing" technologies stood out as the most prominent and extensively studied topics in the intersection of medical informatics and data privacy.

Reeta Sony A.L - National Law University, New Delhi, India. E-mail:reeta.sony@nludelhi.ac.in.Sri Krishna Deva Rao - Professor, National Law University, New Delhi, India. E-mail: psrikrishnadevarao@gmail.com.Bhukya Devi Prasad - Council of Scientific and Industrial Research, New Delhi, India. E-mail: bdeviprasad@csir.res.in. Cloud computing refers to anything that involves delivering hosted services over the Internet. It is fast, cheap, flexible and elastic in nature. In spite of its benefits, cloud computing raises risks for data protection and privacy. One key issue presented by cloud computing is the fact that it changes our thinking of what we consider to be "our data" Data is no longer physically stored on a specific set of computers or servers, but is rather geographically distributed. The globalised nature of cloud computing poses a challenge for personal data protection, which requires a clear location for personal data, an identification of the processor and a responsible individual for data processing. Cloud data can be misused and/or shared with third parties without prior knowledge or consent of the data owner. The direct participation of an individual in transborder data transfer, third party participation in data storage, the processing and transmission of dataand, finally, negligence of data protection due diligence from cloud service providers, makes data protection and privacy laws more relevant. The recent PRISM surveillance programme scandal at the US National Security Agency (NSA) demonstrated the privacy risks that citizens around the world take on when their personal informa­tion is stored and processed in the cloud. This situation requires awell-established techno-legal solution along international standards. India, unlike the European Union, has no dedicated regulatory framework to deal with privacy and personal data protection. Although cloud computing is still in its initial stages in India, existing laws for people who are currently using facilities offeredby cloud service providers are extremely inadequate. However, cloud computing requires legal protection in India under the country's data protection laws, privacy laws and data breach laws, which must meet "international standards'! It is the right time for the Indian government to enact appropriate regulatory frameworks to protect personal data and privacy in the cloud era. The overall objective of this paper is to understand the impact of cloud computing on privacy and personal data protection and to analyse present legal frameworks governing privacy and personal data protection in India and Europe.

Edge computing is widely recognized as a crucial technology for the upcoming generation of communication networks and has garnered significant interest from both industry and academia. Compared to other offloading models like cloud computing, it provides faster data processing capabilities, enhanced security measures, and lower costs by leveraging the proximity of the edge servers to the end devices. This helps mitigate the privacy concerns associated with data transfer in edge computing, by reducing the distance between the data source and the server. Raw data in typical edge computing scenarios still need to be sent to the edge server, leading to data leakage and privacy breaches. Federated Learning (FL) is a distributed model training paradigm that preserves end devices’ data privacy. Therefore, it is crucial to incorporate FL into edge computing to protect data privacy. However, the high training overhead of FL makes it impractical for edge computing. In this study, we propose to facilitate the integration of FL and edge computing by optimizing FL hyper-parameters, which can significantly reduce FL’s training overhead and make it more affordable for edge computing.

Data Classification for Achieving Security in Cloud Computing

Keeping user data private is a huge problem both in cloud computing and computation outsourcing. One paradigm to achieve data privacy is to use tamper-resistant processors, inside which users' private data is decrypted and computed upon. These processors need to interact with untrusted external memory. Even if we encrypt all data that leaves the trusted processor, however, the address sequence that goes off-chip may still leak information. To prevent this address leakage, the security community has proposed ORAM (Oblivious RAM). ORAM has mainly been explored in server/file settings which assume a vastly different computation model than secure processors. Not surprisingly, naively applying ORAM to a secure processor setting incurs large performance overheads.In this paper, a recent proposal called Path ORAM is studied. We demonstrate techniques to make Path ORAM practical in a secure processor setting. We introduce background eviction schemes to prevent Path ORAM failure and allow for a performance-driven design space exploration. We propose a concept called super blocks to further improve Path ORAM's performance, and also show an efficient integrity verification scheme for Path ORAM. With our optimizations, Path ORAM overhead drops by 41.8%, and SPEC benchmark execution time improves by 52.4% in relation to a baseline configuration. Our work can be used to improve the security level of previous secure processors.

Followed by the introduction of IoT and new sustainable technologies, energy management, Quality of Service and decrease of communication costs become important and complex for enterprise systems at airports. The aviation authorities’ reports reveal that the airport ICT investments are mainly focused on travel safety, mobile commerce, and new technologies. The main idea behind a smart airport is to deploy IoT network managed through a Cloud-Fog-Edge paradigm for a smart platform and optimize the airport’s efficiency. An IoT cloud-based platform solution supports multiple types of data, advanced analytics, artificial intelligence, and machine learning techniques. However, cloud computing has certain limitations such as increased delay in data reporting, increased latency in accessing user network, limited customization, increased reliance on external network and data privacy. Fog-Cloud and Edge-Cloud paradigms can overcome the weaknesses of cloud computing architectures. Therefore, to understand the organizational impact of combining the usage of cloud, fog, and edge computing, we created an enterprise architecture that can be applied in a smart airport demonstration study. The enterprise architecture modelling was done by using ArchiMate and validated by means of an expert assessment and prototype implementation.

E-governance can be implemented very effectively using the cloud computing technologies. Security and privacy of data are the major challenges in cloud-based e-governance systems. With proper access control and user revocation schemes, we can protect the privacy and security of data stored in cloud servers used for e-governance. There are a lot of techniques to achieve the controlled access in cloud computing. In some applications of e-government, the users should be allowed to access data, only for a specific period of time. In this paper, we introduce a temporal access control scheme based on attribute-based encryption (ABE), which allows users to access the data according to their access policy and for a specific time period only. Our model includes both the temporal access scheme and a time-based proxy re-encryption scheme to allow a user's access right to expire automatically.

Data privacy is an expected right of most citizens around the world but there are many legislative challenges within a boundary-less cloud computing and World Wide Web environment. Despite its importance, there is limited research around data privacy law gaps and alignment, and the legal side of the security ecosystem which seems to be in a constant effort to catch-up. There are already issues within recent history which show a lack of alignment causing a great deal of confusion, an example of this is the ’right to be forgotten’ case which came up in 2014. This case involved a Spanish man against Google Spain. He requested the removal of a link to an article about an auction for his foreclosed home, for a debt that he had subsequently paid. However, misalignment of data privacy laws caused further complications to the case. This paper introduces the Waikato Data Privacy Matrix, our global project for alignment of data privacy laws by focusing on Asia Pacific data privacy laws and their relationships with the European Union and the USA. This will also suggest potential solutions to address some of the issues which may occur when a breach of data privacy occurs, in order to ensure an individual has their data privacy protected across the boundaries in the Web. With the increase in data processing and storage across different jurisdictions and regions (e.g. public cloud computing), the Waikato Data Privacy Matrix empowers businesses using or providing cloud services to understand the different data privacy requirements across the globe, paving the way for increased cloud adoption and usage.