Introduction to Big Data Analytics
Big data analytics refers to the examination of large volumes of data to identify meaningful connections and make smart decisions. The analytics depends upon statistical methods like regression and clustering to analyze large data sets. The method generally manages large data sets of unstructured information from various organizations. The growth of e-commerce platforms and the usage of smartphones have increased. Big data has the power to predict the behavior of customers and improve the system (Nandhini.P, 2018). Various sources, such as financial transactions, sensors, smart gadgets, and Internet connections, are involved. Big data analytics offers a great technology to enhance real-time insights.
Security and privacy issues in Big Data Analytics
Data breach
The massive data stores in big data systems are susceptible to cyberattacks. If the breach occurs, system exposes various organizational organizations information, such as sensitive and financial information (Ramya, et al., 2023). The method consequently leads to reputational damage towards the organization. The distributed nature of big data increases the impact of a breach. The attackers expose the weak points and target the system. To defend against the systems, ensure strong encryption and real-time monitoring are achieved.
Unauthorized access
Basically, a big data environment involves various access points and users to create opportunities for unauthorized access. Hence, the method leads to information misuse and data corruption. The attackers take privileges and exploit the system. Therefore, using strict policies and multi-factor authentication helps in reducing the security risk.
Data residency and sovereignty
As big data involves various data storage and cloud solutions, it becomes a major issue. Various countries have various data protection laws when they are processing information about the organization. Data residency refers to where the data is located, while data sovereignty deals with jurisdiction and governing the data. Failing the laws consequently leads to damage to the organization with legal concerns.
Poor data governance
The lack of data governance leads to the risk of mismanaging the data. Without the proper rules of information and storage of data, data integrity suffers due to privacy. In complex environments, data flows from various sources, which leads to inconsistent analytics and poor decision-making. So, companies set strong governance and regulate the policies to avoid issues.
De identification limitations
Removing the personal data from data sets is essential for privacy, but it is not enough. As big data becomes more complex, it requires various methodologies, such as privacy and retention of data. However, maintaining the balance between privacy and utility of data continues to be a challenge.
Big data analytics security and privacy issues
Techniques for enhancing data privacy in big data analytics
Encryption techniques
The application of strong encryption methodology is the key element in big data analytics. The techniques, like homomorphic encryption, where the data is presented in the form of encryption without needing a decryption key. Encryption in big data analytics helps to transfer and store the data and defend against unauthorized access. In storage, encryption is the coded format that cannot be understood without the encryption key. In this case, a secure mechanism is used for protecting the network and shielding from hackers. When it comes to analytics, encryption enables a secure computation like homomorphic encryption where mathematical operations are performed.
Access control and authentication
Implementing both strong methodologies and policies helps to ensure authorized access. In a big data system, access control involves using and permission of access the system and users (Ramakrishnan, Sujithra, & Niranjalin, 2024). Authentication helps in verifying data such as biometric scans, multistep verification, and passwords. The predictive measures are useful in a robust access control and authentication system to minimize the risk of unauthorized access.
Data Masking and anonymization
The techniques, such as data masking and anonymization to attain sensitive details. Data masking involves the modification of data points such as credit card numbers, sensitive information, or replacing names (Ngesa, 2024). Minimization helps to remove the identifiable information so that it is not re-identified in the dataset. The privacy techniques help in big data analytics to obtain confidential information and enable organizations to reduce the risk of exposure.
Secure data transmission protocols
Secure data transmission protocols, such as SSL or HTTP, are helpful. Big data environment, where the exchange of data frequently occurs over clusters and cloud services. The security protocol enhances the encryption in the communication channel to maintain confidentiality and integrity during the data transfer. A handshake ensures proper communication between the devices, and the encryption is attained before the data is exchanged.
Techniques for enhancing data privacy in big data analytics
Future Directions of enhancing security and privacy in big data analytics
Designing a holistic privacy and security framework
Future studies aim that by creating all-encompassing frameworks helps in completing the life cycle of big data. the system assists in the analysis and collection of data, which integrates both technical protections and organizational policies. Moreover, ensures a balanced and comprehensive approach towards enhancing security and privacy.
Assessing the real-world impact of security measures
The need for data-driven and experimental research in the future. Furthermore, the methods help manage the case studies and simulations to perform the best. Moreover, helps to identify and promote the best
Practices in the future.
Incorporation of a user-centric approach
Start user attitudes and behaviors towards the privacy of data, install further research would indicate how individuals value the privacy and risk (Rani, 2023). Also, the method incorporates privacy frameworks, which are friendly and accepted solutions.
Conclusion
In Big data analytics, an increase in data flow and an increase in data privacy concerns are observed. In summary, data analytics plays the main role in increasing the decision-making process and enhancing innovation. However, benefits regarding managing privacy concerns. The risks are considered, such as data breaches, algorithmic bias, unauthorized access, and compliance issues. The issues are designed with specific solutions to ensure both security and privacy in the network. To address the challenges, various frameworks like data masking, encryption, and anonymization help in protecting the information. Despite all the obstacles, many cyber techniques exist to enhance the privacy and utility of data.
References
Nandhini.P. (2018). A Research on Big Data Analytics Security and Privacy in Cloud, Data Mining, Hadoop and Mapreduce. Shreyas Satardekar Int. Journal of Engineering Research and Application, 08(04), 65-78. Retrieved from https://www.ijera.com/papers/Vol8_issue4/Part-3/J0804035669.pdf
Ngesa, J. (2024). Tackling security and privacy challenges in the realm of big data analytics. World Journal of Advanced Research and Reviews, 21(02), 552–576. Retrieved from https://wjarr.com/sites/default/files/WJARR-2024-0429.pdf
Ramakrishnan, R., Sujithra, R., & Niranjalin, A. J. (2024). Privacy Challenges and Solutions in Big Data Analytics: A Comprehensive Review. Ijraset Journal For Research in Applied Science and Engineering Technology, 12(05), 2345-2440. Retrieved from https://www.ijraset.com/best-journal/privacy-challenges-and-solutions-in-big-data-analytics-a-comprehensive-review
Ramya, S., Devi, R. S., Pandian, P. S., Suguna, G., Suganya, R., & Manimozhi, N. (2023). ANALYZING BIG DATA CHALLENGES AND SECURITY ISSUES IN DATA. International Research Journal of Modernization in Engineering Technology and Science, 05(01), 421-428. Retrieved from https://www.irjmets.com/uploadedfiles/paper/issue_1_january_2023/32834/final/fin_irjmets1673423558.pdf
Rani, S. (2023). Privacy and Security Issues in Big Data Analytics: Current Challenges and Promising Solutions. International Journal of Scientific Engineering and Research (IJSER), 11(05), 68-71. Retrieved from https://www.ijser.in/archives/v11i5/SE23511130834.pdf
Keywords
Big Data Analytics, Data Breach, Encryption techniques, Unauthorized access, Data masking
Relevant Articles
Big Data and its Application on Insurance Claims Processing and Fraud Detection
Develop Paper on Big Data and Its Application on Health Insurance and Predictive Analytics
Read More About the Topic
Privacy-Preserving Techniques in Big Data Analytics: Challenges and Opportunities