Security- and Privacy-Aware Computing in Cloud With User Mobility

2021 ◽  
pp. 879-897
Author(s):  
Sayani Sen ◽  
Sathi Roy ◽  
Suparna Biswas ◽  
Chandreyee Chowdhury
Keyword(s):  
Data Privacy ◽  
Security And Privacy ◽  
Local Processing ◽  
Processing Unit ◽  
Privacy And Security ◽  
Huge Data ◽  
Learning Techniques ◽  
User Data ◽  
User Data Privacy ◽  
Cloud Servers

Today's computational model has been undergoing a huge paradigm shift from personalized, local processing using local processing unit (LPU) to remote processing at cloud servers located globally. Advances in sensor-based smart applications such as smart home, smart health, smart transport, smart environment monitoring, etc. are generating huge data which needs to stored, pre-processed, analyzed using machine learning and deep learning techniques, which are resource-hungry, to generate results to be saved for future reference, and all these need to be done in real time, with scalability support satisfying user data privacy and security that may vary from application to application. In smart application like remote health monitoring and support, patient data needs utmost privacy besides confidentiality, integrity, and availability.

Security- and Privacy-Aware Computing in Cloud With User Mobility

2020 ◽  
pp. 1-20
Author(s):  
Sayani Sen ◽  
Sathi Roy ◽  
Suparna Biswas ◽  
Chandreyee Chowdhury
Keyword(s):  
Data Privacy ◽  
Security And Privacy ◽  
Local Processing ◽  
Processing Unit ◽  
Privacy And Security ◽  
Huge Data ◽  
Learning Techniques ◽  
User Data ◽  
User Data Privacy ◽  
Cloud Servers

Today's computational model has been undergoing a huge paradigm shift from personalized, local processing using local processing unit (LPU) to remote processing at cloud servers located globally. Advances in sensor-based smart applications such as smart home, smart health, smart transport, smart environment monitoring, etc. are generating huge data which needs to stored, pre-processed, analyzed using machine learning and deep learning techniques, which are resource-hungry, to generate results to be saved for future reference, and all these need to be done in real time, with scalability support satisfying user data privacy and security that may vary from application to application. In smart application like remote health monitoring and support, patient data needs utmost privacy besides confidentiality, integrity, and availability.

scholarly journals Demystifying User Data Privacy in the World of IOT

2019 ◽  
Vol 8 (10) ◽  
pp. 4412-4418
Keyword(s):  
Data Privacy ◽  
Security And Privacy ◽  
Privacy Requirements ◽  
Living Things ◽  
The World ◽  
User Data ◽  
Self Test ◽  
User Data Privacy ◽  
Almost All ◽  
Global World

Internet of Things (IoT) would touch upon almost all aspects of everyday life, as a consequence of which, everything (i.e. living and non-living things) will have a counterpart virtual identities on the internet which would be readable, addressable and locatable. Although it would empower its users with 24×7 connectivity around the global world, unknowingly they would also provide it permission to peep into user’s personal world, which can generate a huge risk on the usability of IoT by users. Thus analyzing the framework of IOT from the perspective of user data protection is a very crucial self-test which is required for IoT implementation. Often the term security and privacy are used interchangeably, but in the IoT environment, both these concept would play a crucial but differentiating role. In this paper, we have scanned the IoT environment with the perspective of privacy requirements, possible threats and the mitigating solutions which are currently in use.

scholarly journals Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Processing Unit ◽  
Privacy And Security ◽  
User Data ◽  
Data Ingestion

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

Mobile Cloud Computing and Its Security and Privacy Challenges

2015 ◽  
pp. 1561-1584
Author(s):  
Hassan Takabi ◽  
Saman Taghavi Zargar ◽  
James B. D. Joshi
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Security And Privacy ◽  
Mobile Cloud ◽  
Computing Environment ◽  
Mobile Terminals ◽  
Privacy And Security ◽  
Cloud Computing Environment ◽  
Timely Fashion ◽  
Cloud Servers

Mobile cloud computing has grown out of two hot technology trends, mobility and cloud. The emergence of cloud computing and its extension into the mobile domain creates the potential for a global, interconnected mobile cloud computing environment that will allow the entire mobile ecosystem to enrich their services across multiple networks. We can utilize significant optimization and increased operating power offered by cloud computing to enable seamless and transparent use of cloud resources to extend the capability of resource constrained mobile devices. However, in order to realize mobile cloud computing, we need to develop mechanisms to achieve interoperability among heterogeneous and distributed devices. We need solutions to discover best available resources in the cloud servers based on the user demands and approaches to deliver desired resources and services efficiently and in a timely fashion to the mobile terminals. Furthermore, while mobile cloud computing has tremendous potential to enable the mobile terminals to have access to powerful and reliable computing resources anywhere and anytime, we must consider several issues including privacy and security, and reliability in realizing mobile cloud computing. In this chapter, the authors first explore the architectural components required to realize a mobile cloud computing infrastructure. They then discuss mobile cloud computing features with their unique privacy and security implications. They present unique issues of mobile cloud computing that exacerbate privacy and security challenges. They also discuss various approaches to address these challenges and explore the future work needed to provide a trustworthy mobile cloud computing environment.

A Generic Privacy Breach Preventing Methodology for Cloud Based Web Service

2015 ◽  
pp. 426-458 ◽  
Author(s):  
S. R. Murugaiyan ◽  
D. Chandramohan ◽  
T. Vengattaraman ◽  
P. Dhavachelvan
Keyword(s):  
Private Information ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Digital Data ◽  
Cloud Data ◽  
Privacy Breach ◽  
Cloud Data Storage ◽  
User Data ◽  
Information Technology Development ◽  
User Data Privacy

The present focuses on the Cloud storage services are having a critical issue in handling the user's private information and its confidentiality. The User data privacy preserving is a vital facet of online storage in cloud computing. The information in cloud data storage is underneath, staid molests of baffling addict endeavor, and it may leads to user clandestine in a roar privacy breach. Moreover, privacy preservation is an indeed research pasture in contemporary information technology development. Preserving User Data in Cloud Service (PUDCS) happens due to the data privacy breach results to a rhythmic way of intruding high confidential digital storage area and barter those information into business by embezzle others information. This paper focuses on preventing (hush-hush) digital data using the proposed privacy preserving framework. It also describes the prevention of stored data and de-identifying unauthorized user attempts, log monitoring and maintaining it in the cloud for promoting allusion to providers and users.

Security and Privacy in Big Data Computing

2020 ◽  
pp. 236-256
Author(s):  
Kiritkumar J. Modi ◽  
Prachi Devangbhai Shah ◽  
Zalak Prajapati
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Cloud Data ◽  
Big Data Privacy ◽  
Cloud Data Storage ◽  
Type Data ◽  
Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

scholarly journals Homomorphic Encryption Based Privacy-Preservation for IoMT

2021 ◽  
Vol 11 (18) ◽  
pp. 8757
Author(s):  
Mikail Mohammed Salim ◽  
Inyeung Kim ◽  
Umarov Doniyor ◽  
Changhoon Lee ◽  
Jong Hyuk Park
Keyword(s):  
Secret Sharing ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Cyber Attacks ◽  
Healthcare Applications ◽  
Mathematical Functions ◽  
Financial Gain ◽  
User Data ◽  
Cloud Servers

Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device–edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.

scholarly journals Developing an App for Cardiovascular Prevention and Scientific Data Collection

2021 ◽  
Author(s):  
Lara Marie Reimer ◽  
Fabian Starnecker ◽  
Heribert Schunkert ◽  
Stephan Jonas
Keyword(s):  
Data Privacy ◽  
Scientific Information ◽  
Scientific Data ◽  
Mobile App ◽  
Long Term Effects ◽  
Privacy And Security ◽  
User Data ◽  
Cvd Prevention ◽  
Acceptance Rates ◽  
The Individual

Background: Mobile apps may encourage a lifestyle that avoids unhealthy behaviors, such as smoking or poor nutrition, which promotes cardiovascular diseases (CVD). Yet, little data is available on the utilization, perception, and long-term effects of such apps to prevent CVD. Objectives: To develop a mobile app concept to reduce the individual CVD risk and collect information addressing research questions on CVD prevention while preserving data privacy and security. Methods: To validate the concept, a prototype will be built, and usability studies will be performed. Results: We expect to determine whether it is possible to reach a broad user base and to collect scientific information while protecting user data sufficiently. Conclusion: To address CVD prevention, we propose a mobile coaching app. We expect high acceptance rates in validation studies.

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