Journal:Privacy preservation techniques in big data analytics: A survey
| Full article title | Privacy preservation techniques in big data analytics: A survey |
|---|---|
| Journal | Journal of Big Data |
| Author(s) | Rao, P. Ram Mohan; Krishna, S. Murali; Kumar, A.P. Siva |
| Author affiliation(s) | MLR Institute of Technology, Sri Venkateswara College of Engineering, JNTU Anantapur |
| Primary contact | Email: rammohan04 at gmail dot com |
| Year published | 2018 |
| Volume and issue | 5 |
| Page(s) | 33 |
| DOI | 10.1186/s40537-018-0141-8 |
| ISSN | 2196-1115 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://link.springer.com/article/10.1186/s40537-018-0141-8 |
| Download | https://link.springer.com/content/pdf/10.1186%2Fs40537-018-0141-8.pdf (PDF) |
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Abstract
Incredible amounts of data are being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology. Not only humans but also machines contribute to data streams in the form of closed circuit television (CCTV) streaming, web site logs, etc. Tons of data is generated every minute by social media and smart phones. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However data analytics is prone to privacy violations. One of the applications of data analytics is recommendation systems, which are widely used by e-commerce sites like Amazon and Flipkart for suggesting products to customers based on their buying habits, leading to inference attacks. Although data analytics is useful in decision making, it will lead to serious privacy concerns. Hence privacy preserving data analytics became very important. This paper examines various privacy threats, privacy preservation techniques, and models with their limitations. The authors then propose a data lake-based modernistic privacy preservation technique to handle privacy preservation in unstructured data.
Keywords: data, data analytics, privacy threats, privacy preservation
Introduction
There is exponential growth in the volume and variety of data due to diverse applications of computers in all domain areas. The growth has been achieved due to affordable availability of computer technology, storage, and network connectivity. The large scale data—which also include person specific private and sensitive data like gender, zip code, disease, caste, shopping cart, religion, etc.—is being stored in a variety of public and private domains. The data holder can then release this data to a third-party data analyst to gain deeper insights and identify hidden patterns which are useful in making important decisions that may help in improving businesses and provide value-added services to customers[1], as well in activities such as prediction, forecasting, and recommendation.[2] One of the prominent applications of data analytics is the recommendation system, which is widely used by e-commerce sites like Amazon and Flipkart for suggesting products to customers based on their buying habits. Facebook does something similar by suggesting friends, places to visit, and even movies to watch based on our interest. However releasing user activity data may lead to inference attacks like identifying gender based on user activity.[3] We have studied a number of privacy preserving techniques which are being employed to protect against privacy threats. Each of these techniques has their own merits and demerits. This paper explores the merits and demerits of each of these techniques and also describes the research challenges in the area of privacy preservation. Always there exists a trade off between data utility and privacy. This paper also proposes a data lake-based modernistic privacy preservation technique to handle privacy preservation in unstructured data with maximum data utility.
Abbreviations
- CCTV: closed circuit television
- MDSBA: multidimensional sensitivity-based anonymization
References
- ↑ Ducange, P.; Pecori, R.; Mezzina, P. (2018). "A glimpse on big data analytics in the framework of marketing strategies". Soft Computing 22 (1): 325–42. doi:10.1007/s00500-017-2536-4.
- ↑ Chauhan, A.; Kummamuru, K.; Toshniwal, D. (2017). "Prediction of places of visit using tweets". Knowledge and Information Systems 50 (1): 145–66. doi:10.1007/s10115-016-0936-x.
- ↑ Yang, D.; Qu, B.; Cudre-Mauroux, P. (2018). "Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation". IEEE Transactions on Knowledge and Data Engineering. doi:10.1109/TKDE.2018.2840974.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.
