Privacy and Security Issues in Big Data by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9789811610073
Publisher: Springer Singapore

8 Conclusion

Big data [2, 46] is evaluated for every bit of insight which contributes in order to provide improved decision making and pragmatic actions with regard to overwhelming enterprises. However, just a significant amount of samples is currently processed. Throughout the paper, we looked at the privacy issues in big data through defining its privacy standards along with subsequently addressing the current privacy-preserving strategies appropriate for big data. The difficulties of privacy in and step of the big data lifecycle were raised together by means of said benefits and downside of current privacy-conserving solutions throughout big data implementations. The present paper further introduces both conventional and modern strategies for the safeguard of privacy in big data. For privacy protection using association rule mining, the principle of hiding a needle amongst haystack was discussed. Ideas of identity-based anonymization and differential privacy and a retrospective analysis of different recent strategies of big data privacy was also explored. It provides robust anonymization methods in the MapReduce context, Which could quickly be expanded by expanding the number of mappers and reducers. In the future path, insights are required to achieve successful solutions to both the robustness of privacy and protection issues throughout the age of big data and, in particular, the task of conciliating security and privacy models by leveraging the map reduces the scope. Then, in this paper, policies and rights regarding big data standards such as Cross-Border Transfer of Personal Data, The Right to be Forgotten, Rights to Objection, Restriction, and Portability were also discussed. Along with that, a brief review of the Indian Personal Data Protection Bill of 2018 was presented. Differential privacy is one of the spheres that have a great deal of latent ability to be further manipulated. There are a lot of issues when the Internet of things and big data came along, even with the exponential development of IoT; the data volume is high, but the accuracy is poor and information belongs to multiple data vendors, who undoubtedly have a wide range of different styles and modes of representation. And data, as structured, semi-structured, and sometimes entirely unstructured, is heterogeneous. This raises new threats to privacy and brings up analysis concerns. Thus, going forward, it is possible to research and implement new ways of protecting mining safety. As such, there exists a great deal of space for more study into privacy conservation approaches in big data.

References

1.

Kolomvatsos K, Anagnostopoulos C, Hadjiefthymiades IS (2015) An efficient time optimized scheme for progressive analytics in big data. Big Data Res 2(4):155–165Crossref

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