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Journal articles on the topic 'Data security and Data privacy'

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Published: 1 June 2024

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1

Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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2

Hennessy, S. D., G. D. Lauer, N. Zunic, B. Gerber, and A. C. Nelson. "Data-centric security: Integrating data privacy and data security." IBM Journal of Research and Development 53, no. 2 (March 2009): 2:1–2:12. http://dx.doi.org/10.1147/jrd.2009.5429044.

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3

Suleiman, James, and Terry Huston. "Data Privacy and Security." International Journal of Information Security and Privacy 3, no. 2 (April 2009): 42–53. http://dx.doi.org/10.4018/jisp.2009040103.

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4

Gaff, Brian M., Thomas J. Smedinghoff, and Socheth Sor. "Privacy and Data Security." Computer 45, no. 3 (March 2012): 8–10. http://dx.doi.org/10.1109/mc.2012.102.

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5

Adam, J. A. "Data security-cryptography=privacy?" IEEE Spectrum 29, no. 8 (August 1992): 29–35. http://dx.doi.org/10.1109/6.144533.

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6

S, Surya Prasad, and Gobi Natesan. "Ensuring Data Security and Privacy in Cloud Infrastructure." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 5012–16. http://dx.doi.org/10.55248/gengpi.5.0324.0817.

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Kumar.R, Dr Prasanna, Porselvan G, Prem Kumar S, and Robinlash F. "Security and Privacy Based Data Sharing in Cloud Computing." International Journal of Innovative Research in Engineering & Management 5, no. 1 (January 2018): 42–49. http://dx.doi.org/10.21276/ijirem.2018.5.1.9.

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George, Jomin, and Takura Bhila. "Security, Confidentiality and Privacy in Health of Healthcare Data." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 373–77. http://dx.doi.org/10.31142/ijtsrd23780.

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Kapil, Gayatri, Alka Agrawal, and R. A. Khan. "Big Data Security and Privacy Issues." Asian Journal of Computer Science and Technology 7, no. 2 (August 5, 2018): 128–32. http://dx.doi.org/10.51983/ajcst-2018.7.2.1861.

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Big data gradually become a hot topic of research and business and has been growing at exponential rate. It is a combination of structured, semi-structured & unstructured data which is generated constantly through various sources from different platforms like web servers, mobile devices, social network, private and public cloud etc. Big data is used in many organisations and enterprises, big data security and privacy have been increasingly concerned. However, there is a clear contradiction between the large data security and privacy and the widespread use of big data. In this paper, we have indicated challenges of security and privacy in big data. Then, we have presented some possible methods and techniques to ensure big data security and privacy.

10

Danish, Muhammad. "Big Data Security And Privacy." International Journal of Computer Trends and Technology 67, no. 5 (May 25, 2019): 20–26. http://dx.doi.org/10.14445/22312803/ijctt-v67i5p104.

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Martucci, William C., and Jennifer K. Oldvader. "Workplace privacy and data security." Employment Relations Today 37, no. 2 (July 13, 2010): 59–66. http://dx.doi.org/10.1002/ert.20299.

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12

Al-Museelem, Waleed, and Chun Lin Li. "Data Security and Data Privacy in Cloud Computing." Advanced Materials Research 905 (April 2014): 687–92. http://dx.doi.org/10.4028/www.scientific.net/amr.905.687.

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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.

13

Eghmazi, Ali, Mohammadhossein Ataei, René Jr Landry, and Guy Chevrette. "Enhancing IoT Data Security: Using the Blockchain to Boost Data Integrity and Privacy." IoT 5, no. 1 (January 10, 2024): 20–34. http://dx.doi.org/10.3390/iot5010002.

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The Internet of Things (IoT) is a technology that can connect billions of devices or “things” to other devices (machine to machine) or even to people via an existing infrastructure. IoT applications in real-world scenarios include smart cities, smart houses, connected appliances, shipping, monitoring, smart supply chain management, and smart grids. As the number of devices all over the world is increasing (in all aspects of daily life), huge amounts of data are being produced as a result. New issues are therefore arising from the use and development of current technologies, regarding new applications, regulation, cloud computing, security, and privacy. The blockchain has shown promise in terms of securing and preserving the privacy of users and data, in a decentralized manner. In particular, Hyperledger Fabric v2.x is a new generation of blockchain that is open source and offers versatility, modularity, and performance. In this paper, a blockchain as a service (BaaS) application based on Hyperledger Fabric is presented to address the security and privacy challenges associated with the IoT. A new architecture is introduced to enable this integration, and is developed and deployed, and its performance is analyzed in real-world scenarios. We also propose a new data structure with encryption based on public and private keys for enhanced security and privacy.

14

Bourvil and Levi. "Multi-Level Trust Privacy Preserving Data Mining to Enhance Data Security and Prevent Leakage of the Sensitive Data." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 2 (May 30, 2017): 21–25. http://dx.doi.org/10.9756/bijiems.8327.

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15

Smith, J. H., and JS Horne. "Data privacy and DNA data." IASSIST Quarterly 47, no. 3-4 (December 14, 2023): 1–3. http://dx.doi.org/10.29173/iq1094.

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The letter to the Editor is in response to the manuscript by Hertzog et al. (2023) titled "Data management instruments to Protect the personal information of Children and Adolescents in sub-Saharan Africa." The letter elaborates on personal data protection, particularly the POPI Act's data management requirements; the DNA Act mandates specific measures to ensure the data integrity and security of the NFDD's information. In addition, it criminalises the misuse or compromise of the data's integrity within the NFDD. In addition, the DNA Act established the National Forensic Oversight and Ethical Board (NFOEB), which is responsible for overseeing ethical compliance, implementing the Act, and preserving data integrity within the NFDD. The NFOEB is also responsible for investigating any complaints regarding DNA forensics and the management of the NFDD.

16

Yang, Jing, Lianwei Qu, and Yong Wang. "Multidomain Fusion Data Privacy Security Framework." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–26. http://dx.doi.org/10.1155/2021/8492223.

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With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, classification, and grading model, determine the attributes sensitivity and relationship between attributes to realize the multimode data statistics. Second, combine them with the different modal histograms to build multimodal histograms. Finally, we propose a privacy protection model to ensure the security of data publishing. The experimental analysis shows that the framework can not only build multimodal histograms of different microdomain attribute sets but also effectively reduce frequency query error.

17

Kumar, Raj, and Sushma Pal. "Data security and privacy through stenography." Global Sci-Tech 10, no. 2 (2018): 98. http://dx.doi.org/10.5958/2455-7110.2018.00017.4.

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Taitsman, Julie K., Christi Macrina Grimm, and Shantanu Agrawal. "Protecting Patient Privacy and Data Security." New England Journal of Medicine 368, no. 11 (March 14, 2013): 977–79. http://dx.doi.org/10.1056/nejmp1215258.

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19

Yan, Zheng, Willy Susilo, Elisa Bertino, Jun Zhang, and Laurence T. Yang. "AI-driven data security and privacy." Journal of Network and Computer Applications 172 (December 2020): 102842. http://dx.doi.org/10.1016/j.jnca.2020.102842.

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20

Xiang, Yang, Man Ho Au, and Miroslaw Kutylowsky. "Security and privacy in big data." Concurrency and Computation: Practice and Experience 28, no. 10 (March 31, 2016): 2856–57. http://dx.doi.org/10.1002/cpe.3796.

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Mandal, Sanjeev Kumar, Amit Sharma, Santosh Kumar Henge, Sumaira Bashir, Madhuresh Shukla, and Asim Tara Pathak. "Secure data encryption key scenario for protecting private data security and privacy." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 2 (2024): 269–81. http://dx.doi.org/10.47974/jdmsc-1881.

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Cryptography, specifically encryption, plays a pivotal role in protecting data from unauthorized access. However, not all encryption methods are equally effective, as some exhibit vulnerabilities. This research proposing a novel encryption method that builds upon established techniques to enhance data security. The proposed method combines the strengths of the Festial encryption method and the Advanced Encryption Standard (AES) to create an algorithm that exhibits superior resistance against attacks. The proposed encryption method successfully mitigates vulnerabilities, demonstrating enhanced resilience against unauthorized access attempts and minimizing the potential for data leakage. By prioritizing security and advancing encryption technologies, it can effectively protect personal information, maintain data confidentiality and integrity, and establish a safer digital environment for individuals and organizations.

22

Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, and Yong Ren. "Information Security in Big Data: Privacy and Data Mining." IEEE Access 2 (2014): 1149–76. http://dx.doi.org/10.1109/access.2014.2362522.

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23

Wu, Xuanting, and Yi Chen. "Research on Personal Data Privacy Security in the Era of Big Data." Journal of Humanities and Social Sciences Studies 4, no. 3 (September 6, 2022): 228–35. http://dx.doi.org/10.32996/jhsss.2022.4.3.24.

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Big data privacy security has become a hot research topic in contemporary society. Based on the data relevance and life-cycle in the era of big data, this paper analyzes the causes of security problems in China’s data privacy. It puts forward suggestions from three aspects to provide references for subsequent research. Based on the current research progress, this paper first sorts out the definitions of data privacy and data privacy protection, then summarizes the causes of privacy security from the perspectives of technology and management and reveals the consequences of data privacy security issues. The demonstrated results trigger an insight into the solution strategy of data privacy problems and offer suggestions for solving problems from the perspective of management based on the data life-cycle model. Finally, starting from other stages of the data life-cycle and the application scenarios of big data, this paper looks forward to the future research direction. This study found that the present study needs to focus on the combination of system and technology, the improvement of laws and regulations, and the data life-cycle model in both technical and institutional management.

24

Liu, Jinyang. "An overview of big data mining and data privacy protection technologies." Applied and Computational Engineering 21, no. 1 (October 23, 2023): 187–92. http://dx.doi.org/10.54254/2755-2721/21/20231143.

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With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection techniques. In addition, this paper provides a summary of the most important privacy protection methods. These techniques include data anonymization, association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.

25

Harbola, Aditya. "Use of data auditing for encrypted data stored in cloud environment." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 293–302. http://dx.doi.org/10.17762/msea.v70i1.2311.

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The exponential growth of data generation and the increasing reliance on cloud-based storage solutions have raised significant concerns regarding data security and privacy. The widespread adoption of encryption techniques has been effective in protecting data confidentiality, but it also introduces new challenges in terms of data auditing. This paper explores the use of data auditing techniques for encrypted data stored in cloud environments to ensure data integrity, availability, and accountability while preserving privacy. We first provide an overview of the current state of cloud storage security and encryption techniques, followed by a discussion on the importance of data auditing for encrypted data. The paper then delves into existing data auditing approaches, specifically focusing on Public Key-based Auditing (PKA) and Private Key-based Auditing (PrKA) schemes. We examine their advantages, drawbacks, and suitability for different scenarios, highlighting their ability to maintain data privacy without compromising the auditing process. To address the limitations of current auditing techniques, we propose an innovative hybrid auditing framework that combines the strengths of PKA and PrKA schemes. Our approach enables efficient data auditing while ensuring data confidentiality, integrity, and privacy. It also supports dynamic data operations, including insertion, deletion, and modification, allowing for seamless adaptation to various cloud storage environments. We validate the effectiveness of our proposed hybrid auditing framework through a series of experiments, comparing it with existing PKA and PrKA solutions. The results demonstrate the superiority of our framework in terms of security, privacy preservation, and performance, making it a promising solution for auditing encrypted data in cloud environments.

26

Kumar, K. Praveen. "Efficient Encryption Algorithm for Data Security in Big Data Cloud Environment." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1127–31. http://dx.doi.org/10.22214/ijraset.2021.38995.

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Abstract: High speed Internet technology development and data technologies create a huge amount of information in day by day. Use of big data and cloud both are managing traditional data processing and storage issues. At the same time users can face many issues like data security and privacy manner. Here we proposed Blockchain based secure algorithm for to achieve Security and Privacy for big data. Keywords: Security, Blockchain, Big data and Cloud environment

27

Livshitz, Ilya. "Data privacy assurance for remote work." Energy Safety and Energy Economy 1 (February 2022): 57–62. http://dx.doi.org/10.18635/2071-2219-2022-1-57-62.

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Today, more employees are working remotely than ever before. Remote work poses unique security challenges for companies including data privacy assurance challenges. In this paper, current statistics of national and international expert communities demonstrates resent trends and practices in personal data privacy assurance. The author’s experiences of information security audits show certain violations of current regulatory limits. The results presented in this paper can be used for planning, conducting, and evaluating information during security audits, especially when it comes to personal data.

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Shahid, Arsalan, Thien-An Ngoc Nguyen, and M.-Tahar Kechadi. "Big Data Warehouse for Healthcare-Sensitive Data Applications." Sensors 21, no. 7 (March 28, 2021): 2353. http://dx.doi.org/10.3390/s21072353.

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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Gatkal, Suyog, Vinayak Dhage, Dhanashree Kalekar, and Sanket Ghadge. "Survey on Medical Data Storage Systems." International Journal of Soft Computing and Engineering 11, no. 1 (September 30, 2021): 44–48. http://dx.doi.org/10.35940/ijsce.a3528.0911121.

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Nowadays digital data storage and digital communication are widely used in the healthcare sector. Since data in the digital form significantly easier to store, retrieve, manipulate, analyses, and manage. Also, digital data eliminate the threat of data loss considerably. These advantages pushing many hospitals to store their data digitally. But, as the patients reveal their private and important information to the doctor, it is very crucial to maintain the privacy, security, and reliability of the healthcare data. In this process of handling the data securely, several technologies are being used like cloud storage, data warehousing, blockchain, etc. The main aim of this survey is to study the different models and technologies in the healthcare sector and analyses them on different parameters like security, privacy, performance, etc. This study will help the new developing healthcare systems to choose appropriate technology and approach to build a more efficient, robust, secure, and reliable system.

30

Thanh Chi Phan and Hung Chi Tran. "Consideration of Data Security and Privacy Using Machine Learning Techniques." International Journal of Data Informatics and Intelligent Computing 2, no. 4 (December 19, 2023): 20–32. http://dx.doi.org/10.59461/ijdiic.v2i4.90.

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As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data.

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J A, Smitha, Rakshith G Raj, Sumanth J Samuel, and Pramath P Yaji. "Security Measures of Textual Data." International Journal of Innovative Research in Information Security 9, no. 03 (June 23, 2023): 55–60. http://dx.doi.org/10.26562/ijiris.2023.v0903.01.

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As the volume and importance of textual data in data science continues to grow, combined with advancements in its techniques, it has created numerous opportunities for extracting valuable insights from textual information. However, privacy and the security of private data are issues that are brought up by the analysis of text data. This paper presents an in-depth analysis of security measures specifically designed for protecting textual data in data science applications. It explores various techniques and strategies to safeguard the confidentiality, integrity, and availability of textual data throughout its lifecycle. The objective is to provide data scientists and practitioners with a comprehensive understanding of the security challenges and best practices associated with textual data in the context of data science.

32

Chan, Tom, Concetta Tania Di Iorio, Simon De Lusignan, Daniel Lo Russo, Craig Kuziemsky, and Siaw-Teng Liaw. "UK National Data Guardian for Health and Care’s Review of Data Security: Trust, better security and opt-outs." Journal of Innovation in Health Informatics 23, no. 3 (December 20, 2016): 627. http://dx.doi.org/10.14236/jhi.v23i3.909.

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Sharing health and social care data is essential to the delivery of high quality health care as well as disease surveillance, public health, and for conducting research. However, these societal benefits may be constrained by privacy and data protection principles. Hence, societies are striving to find a balance between the two competing public interests. Whilst the spread of IT advancements in recent decades has increased the demand for an increased privacy and data protection in many ways health is a special case.UK, are adopting guidelines, codes of conduct and regulatory instruments aimed to implement privacy principles into practical settings and enhance public trust. Accordingly, in 2015, the UK National Data Guardian (NDG) requested to conduct a further review of data protection, referred to as Caldicott 3. The scope of this review is to strengthen data security standards and confidentiality. It also proposes a consent system based on an “opt-out” model rather than on “opt-in.Across Europe as well as internationally the privacy-health data sharing balance is not fixed. In Europe enactment of the new EU Data Protection Regulation in 2016 constitute a major breakthrough, which is likely to have a profound effect on European countries and beyond. In Australia and across North America different ways are being sought to balance out these twin requirements of a modern society - to preserve privacy alongside affording high quality health care for an ageing population. Whilst in the UK privacy legal framework remains complex and fragmented into different layers of legislation, which may negatively impact on both the rights to privacy and health the UK is at the forefront in the uptake of international and EU privacy and data protection principles. And, if the privacy regime were reorganised in a more comprehensive manner, it could be used as a sound implementation model for other countries.

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Qin, Peng, Wei Li, and Ke Ding. "A Big Data Security Architecture Based on Blockchain and Trusted Data Cloud Center." Wireless Communications and Mobile Computing 2022 (August 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/7272405.

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In view of the shortcomings of big data security and privacy protection in cloud environment, a big data security architecture was proposed in this paper. Based on blockchain technology and trusted data cloud center, data security architecture adopts the ideas of trusted authentication, intrusion detection, data segmentation, and decentralized storage and applies Amazon AWS log processing service, PairHand user authentication protocol, and Hadoop data analysis framework to realize dig data security and privacy protection in the cloud environment. This paper realizes system initialization and user authentication, hierarchical data storage, decentralized storage, and user security access. The experimental results show that the system architecture can ensure data security and data access speed, which can provide necessary reference for cloud security.

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N.Maniam, Jacentha, and Dalbir Singh. "TOWARDS DATA PRIVACY AND SECURITY FRAMEWORK IN BIG DATA GOVERNANCE." International Journal of Software Engineering and Computer Systems 6, no. 1 (May 31, 2020): 41–51. http://dx.doi.org/10.15282/ijsecs.6.1.2020.5.0068.

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Yao, Lu. "Holistic Data Security: A Balanced Approach to Data and Privacy." Információs Társadalom 23, no. 4 (December 31, 2023): 102. http://dx.doi.org/10.22503/inftars.xxiii.2023.4.7.

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The expansion of the data economy has raised a multitude of concerns that scholars worldwide are working towards overcoming. Given the divergent views on data in fields such as law, economics, and sociology, varying approaches to data governance have been suggested. However, regardless of the approach chosen, the multi- dimensional aspects of data should not be disregarded. This book incorporates various research viewpoints on data governance and introduces the innovative notion of “Holistic Data Security”, which can offer fresh avenues for exploration by academics across diverse fields of study.

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Shobha Rani, P., and Vigneswari D. "SECURITY AND PRIVACY IN BIG DATA ANALYTICS." International Journal on Intelligent Electronic Systems 10, no. 2 (2016): 32–35. http://dx.doi.org/10.18000/ijies.30155.

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Atoum, Ibrahim A., and Ismail M. Keshta. "Big data management: Security and privacy concerns." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 5 (May 2021): 73–83. http://dx.doi.org/10.21833/ijaas.2021.05.009.

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Big data has been used by different companies to deliver simple products and provide enhanced customer insights through predictive technology such as artificial intelligence. Big data is a field that mainly deals with the extraction and systemic analysis of large data sets to help businesses discover trends. Today, many companies use Big Data to facilitate growth in different functional areas as well as expand their ability to handle large customer databases. Big data has grown the demand for information management experts such that many software companies are increasingly investing in firms that specialize in data management and analytics. Nevertheless, the issue of data protection or privacy is a threat to big data management. This article presents some of the major concerns surrounding the application and use of Big Data about challenges of security and privacy of data stored on technological devices. The paper also discusses some of the current studies being undertaken aimed at addressing security and privacy issues in Big Data.

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Akinwunmi, O. O., S. A. Onashoga, and O. Folorunso. "Employing differential privacy for big data security." Journal of Computer Science and Its Application 26, no. 2 (February 11, 2020): 134. http://dx.doi.org/10.4314/jcsia.v26i2.13.

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Guan, Yunguo, Jun Shao, Guiyi Wei, and Mande Xie. "Data Security and Privacy in Fog Computing." IEEE Network 32, no. 5 (September 2018): 106–11. http://dx.doi.org/10.1109/mnet.2018.1700250.

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Matturdi, Bardi, Xianwei Zhou, Shuai Li, and Fuhong Lin. "Big Data security and privacy: A review." China Communications 11, no. 14 (2014): 135–45. http://dx.doi.org/10.1109/cc.2014.7085614.

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Win, Khin Than, and Willy Susilo. "Information security and privacy of health data." International Journal of Healthcare Technology and Management 7, no. 6 (2006): 492. http://dx.doi.org/10.1504/ijhtm.2006.010413.

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Li, Yan, Young-Sik Jeong, Byeong-Seok Shin, and Jong Hyuk Park. "Crowdsensing Multimedia Data: Security and Privacy Issues." IEEE MultiMedia 24, no. 4 (October 2017): 58–66. http://dx.doi.org/10.1109/mmul.2017.4031306.

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SIMPSON, ROY L. "Ensuring Patient Data, Privacy, Confidentiality and Security." Nursing Management (Springhouse) 25, no. 7 (July 1994): 18???22. http://dx.doi.org/10.1097/00006247-199407000-00004.

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priya.E, Shanmuga, and R. Kavi tha. "Big Data Security and Privacy- A Survey." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 150–54. http://dx.doi.org/10.14445/22312803/ijctt-v49p123.

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Bertino, Elisa. "Editorial: Introduction to Data Security and Privacy." Data Science and Engineering 1, no. 3 (September 2016): 125–26. http://dx.doi.org/10.1007/s41019-016-0021-1.

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Брижко, В. М., and В. Г. Пилипчук. "Privacy, confidentiality and security of personal data." INFORMATION AND LAW, no. 1(32) (February 20, 2020): 33–46. http://dx.doi.org/10.37750/2616-6798.2020.1(32).200304.

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T.K, Adarsh, and R. Jebakumar Dr. "Security & privacy in IoT Data Provenance." International Journal of Engineering and Technology 10, no. 3 (June 30, 2018): 843–47. http://dx.doi.org/10.21817/ijet/2018/v10i3/181003085.

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Sun, Yunchuan, Junsheng Zhang, Yongping Xiong, and Guangyu Zhu. "Data Security and Privacy in Cloud Computing." International Journal of Distributed Sensor Networks 10, no. 7 (January 2014): 190903. http://dx.doi.org/10.1155/2014/190903.

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Pant, Abhishek. "Importance of Data Security and Privacy Compliance." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 1561–65. http://dx.doi.org/10.22214/ijraset.2023.56862.

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Abstract:

Abstract: In an era characterized by an unprecedented proliferation of digital information, the safeguarding of sensitive data has emerged as a paramount concern for individuals, businesses, and governments alike. This abstract delves into the critical importance of data security and privacy compliance in contemporary society. As technological advancements continue to redefine the landscape of data generation, collection, and utilization, the potential risks and vulnerabilities associated with unauthorized access and misuse of information have escalated. This paper highlights the multifaceted significance of data security and privacy compliance across various domains. Firstly, it explores the imperative of protecting personal information to uphold individual privacy rights and maintain public trust. The escalating frequency and sophistication of cyber threats underscore the necessity for robust data security measures to thwart unauthorized access, data breaches, and identity theft.

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Ramachandra, Mohan Naik, Madala Srinivasa Rao, Wen Cheng Lai, Bidare Divakarachari Parameshachari, Jayachandra Ananda Babu, and Kivudujogappa Lingappa Hemalatha. "An Efficient and Secure Big Data Storage in Cloud Environment by Using Triple Data Encryption Standard." Big Data and Cognitive Computing 6, no. 4 (September 26, 2022): 101. http://dx.doi.org/10.3390/bdcc6040101.

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Abstract:

In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.