Privacy and Security of Big Data

The ACM 1st International Workshop on Privacy and Security of Big Data (PSBD 2014), held in Shanghai, China on November 7, 2014, in conjunction with the ACM 23rd International Conference on Information and Knowledge Management (CIKM 2014), presents research on privacy and security of big data, an emerging challenge in actual database and data mining research. PSBD 2014 program has two interesting s/essions on (i) scalable privacy-preserving and security-control methods for big data processing, and (ii) user-oriented and data-oriented privacy methods for big data processing, plus a panel discussing current challenges and future research perspectives of privacy and security of big data.

To study and solve the issues of big data security and privacy protection is the key to promote the healthy development of big data. In this paper, a 3D big data security and privacy protection model based on life cycle, security goal and user role is presented, and the security challenges and security goals of big data are analyzed from different aspects. The feasible technology and its application in big data security and privacy protection are expounded and analyzed, and the in-depth research direction of big data security and privacy protection is discussed and summarized.

An Overview of Security and Privacy Issues in Big Data

Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organisations and individuals by improving productivity and enriching people's life. However, security and privacy are big concerns for big data applications. While a large quantity of data is collected, securely storing, processing and using the data are challenging. In this paper, we propose a four-factor framework for big data security and privacy in business information systems. The proposed framework addresses big data security and privacy issues in terms of collecting the right data, collecting the right amount of data, protecting the data in the right way, and using the data for the right purposes. We present a set of approaches and models for each of the four factors to improve big data security and privacy.

Security and privacy issues are magnified by the volume, variety, and velocity of Big Data, such as Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, Big Data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, Big Data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the Big Data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging Big Data security and privacy issues.

Due to the proliferation of the Internet, IoT, and Cloud Computing, there is now an abundance of virtual data in every industry, field of study, and government agency. Big data has quickly become a topic of intense interest, garnering media coverage and commentary from all over the world. Data privacy and security in Big Data is a pressing concern. The 5Vs of big data—size, velocity, value, veracity, and variety—lower the bar for adequate protection. This paper aimed to draw attention to security and privacy issues and challenges associated with Big Data in healthcare, the resolution of which could result in a more secure data processing and computing infrastructure. This paper also provides a high-level overview of the K-Anonymity technique for protecting the privacy of large datasets before they are released for analysis, with the goal of preventing the disclosure of personally identifiable information. In conclusion, this paper summarizes the functions and features of the best big data security solutions offered by industry leaders.

The growing adoption of big data technologies in healthcare has significantly enhanced patient care, diagnostics, and system efficiency. Yet, this transformation brings with it serious concerns about the security and privacy of sensitive medical information. The health care industry is in a critical phase of intelligence with the rapid advancement of modern information technology. With the increased use of healthcare big data, the issue of information security is increasingly becoming critical in the management of smart healthcare care, including the leak of patient privacy, the most critical issue. Thus, the enhancement of information management of intelligent health care during the age of big data is a significant aspect of long-term sustainable development of the hospitals. This paper has initially determined the most influential indicators to influence the privacy disclosure of big data in managing health care and presented a comprehensive overview of the current landscape in healthcare data security and thereafter set the privacy and security based access control model, which has been applied on the security and management of big data in utilization of medical data, and solves the issue of actual data breach where actual problems are involved. Lastly, the model is contrasted with the state-of-the-art techniques. The paper offers a comparative analysis of proposed solution considering numerous parameters and highlights critical gaps for building more secure and trustworthy healthcare systems. The findings confirm that the model is useful in the evaluation of the existing safety threats and forecasting the scope of the various risk factors, by demonstrating that Network Segmentation and Cloud Usage (Hybrid) have significantly enhanced the results by 1.5% and 6.7% respectively and the User Access mechanism is upgraded by 1.5% and 6.7% respectively. The Audit Trail and Compliance is also improving as the proposed technique examines ACL and explores key global regulatory frameworks like the GDPR and HIPAA. The outcome of this study suggests that the proposed access control model is resistant to most cyber-attacks in big data, and it is also demonstrated that the offered framework can be used as a starting point in order to develop secure and safe medical big data solutions. Therefore, this study can be valuable to future scholars to understand the information about the security and privacy of big data in the medical field and ways to implement countermeasures.

In order to protect the security and privacy of big data, the cloud storage service needs to enforce effective access control mechanism on user requests. Attribute-Based Encryption is a promising cryptographic access control technique to ensure the end-to-end security of data in cloud. However, the existing ABE researches mainly focus on the efficiency decryption, while the flexibility of policy, the communication cost, and the metadata management of ciphertexts are still challenging issues in the big data environment. In this paper, for the first time, we propose a new distributed, scalable and fine-grained access control scheme based on classification attributes for the cloud object storage. The classification attributes and threshold policies are integrated into an access structure, and then the objects are encrypted with the integrated access structure. The constant-size ciphertext components related to attributes can be managed as the corresponding metadata. As a result the encryption complexity and ciphertext storage are reduced. In addition, we present a new label-based access control model with multi-authorities to describe the detailed relationships of entities in our scheme. Besides, the proposed scheme is proved to be secure under l-BDHE assumption, and the system implementation demonstrates the practical feasibility and good performance.

In view of the wide application and popularization of large data, more and more data security and privacy issues have brought great challenges to the development of large data. Starting from the characteristics of big data, this paper analyses various risks of information security, and puts forward the corresponding development strategy of big data security. The results show that the combination of technology and relevant policies and regulations can better solve the problem of big data security and privacy protection.

The "Big Data" era brings massive data inflows from diverse sources, creating opportunities and privacy/security challenges. With rising public concern due to frequent data breaches, protecting privacy and data security has become crucial. This paper summarizes current academic research and developments in data protection technologies, including encryption, anonymization, and access control. It aims to provide insights into data security and privacy in big data, examining future technological prospects and challenges.

In this era, big data is the most common buzzword across different industries due to its capabilities of collecting, processing, storing and analysing data. The advancement of the E-Commerce paved the way for merchants and customers to meet online to satisfy their requirements by exchanging goods and services at a reasonable cost. The challenges and opportunities for big data on the emphasis of data privacy and security is a widely discussed topic among businesses especially E-Commerce merchants. There are several reviews available on emphasizing big data opportunities and challenges with regard to privacy and security. However, a comprehensive review on E-Commerce highlighting thematically on the tools and technologies is not given enough consideration. Therefore, the purpose of this study is to review the state-of-the-art technologies towards privacy and security in the E-Commerce platforms. The identified cryptographic technologies were also discussed with the rational standpoint to understand the viability to apply in the E-Commerce operations. The study concludes with an enlightening path from which the E-Commerce merchants can be vigilant on data privacy and security in future.

Social networking big data: Opportunities, solutions, and challenges

This paper presents a review of ISO standards and real-world case studies to provide a robust framework for enhancing big data security and privacy in libraries. The study synthesises relevant ISO standards such as ISO/IEC 27001, ISO/IEC 27014, and ISO/IEC TS 19608 and analyses library cyberattacks to identify vulnerabilities and security gaps. The paper provides practical recommendations and mitigation strategies particularly beneficial for librarians in resource-limited regions and developing countries. By integrating these standards with case study insights and addressing security requirements across the big data lifecycle, the paper offers an actionable guide to help libraries strengthen their cybersecurity frameworks, improve risk management, and maintain public trust.

2018 International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec)

Security and Privacy in Social Networks and Big Data