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.
Privacy threats in data analytics
Privacy is the ability of an individual to determine what data can be shared, and employ access control. If the data is in the public or private domain, then it is a threat to individual privacy as the data is held by a data holder. The data holder can be a social networking application, website, mobile app, e-commerce site, bank, hospital, etc. It is the responsibility of the data holder to ensure privacy of the users data. Apart from the data held in various domains, knowingly or unknowingly users may contribute to data leakage. For example, most of mobile apps seek access to our contacts, files, camera, etc., and without reading the privacy statement we agree to all its terms and conditions, there by contributing to data leakage.
Hence there is a need to educate smart phone users regarding privacy and privacy threats. Some of the key privacy threats include (1) surveillance, (2) disclosure, (3) discrimination, and (4) personal embracement and abuse.
Surveillance
Many retail, e-commerce, etc. businesses study their customers' buying habits and try to come up with various offers and value-added services.[4] Based on the opinion data and sentiment analysis, social media sites may provide recommendations of new friends, places to visit, people to follow, etc. This is possible only when they continuously monitor their customers' transactions. This is a serious privacy threat as no individual accepts surveillance.
Disclosure
Consider a hospital holding a patient's data, often containing identifying or revealing information such as zip code, gender, age, and disease.[5][6][7] The data holder, the hospital, has released data to a third party for analysis by anonymizing sensitive personal information so that the person cannot be identified. The third party data analyst can map this information with freely available external data sources like census data and then identify the person suffering a disorder. This is how the private information of a person can be disclosed, which is considered to be a serious privacy breach.
Discrimination
Discrimination is the bias or inequality which can happen when some private information of a person is disclosed. For instance, statistical analysis of electoral results proved that people of one community were completely against the party, which formed the government. Now the government can neglect that community or can have bias over them.
Personal embracement and abuse
Whenever a person's private information is disclosed, it can even lead to personal embracement or abuse. For example, a person was privately taking medication for some specific problem and was buying the medicine on a regular basis from a medical shop. As part of their regular business model, the medical shop may send a reminder and offers related to the medicine over the phone. If another family member has noticed this, it may lead to personal embracement and even abuse.[8]
Data analytics activity creates data privacy issues. Many countries are enacting and enforcing privacy preservation laws. Yet something as simple as lack of awareness can still generate privacy attacks despite these mechanisms. For example, many smart phones users are not aware of the information that is stolen from their phones by many apps. Previous research shows only 17 percent of smart phone users are aware of such privacy threats.[9]
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.
- ↑ Liu, Y.; Guo, W.; Fan, C.-I. et al. (2018). "A Practical Privacy-Preserving Data Aggregation (3PDA) Scheme for Smart Grid". IEEE Transactions on Industrial Informatics. doi:10.1109/TII.2018.2809672.
- ↑ Duncan, G.T.; Fienberg, S.E.; Krishnan, R. et al. (2001). "Disclosure limitation methods and information loss for tabular data". In Doyle, P.; Lane, J.; Theeuwes, J. et al.. Confidentiality, disclosure and data access: Theory and practical applications for statistical agencies. Elsevier. pp. 135–66. ISBN 9780444507617.
- ↑ Duncan, G.T.; Lambert, D. (1986). "Disclosure-Limited Data Dissemination". Journal of the American Statistical Association 81 (393): 10-18. doi:10.1080/01621459.1986.10478229.
- ↑ Lambert, D. (1993). "Measures of disclosure risk and harm". Journal of Official Statistics 9 (2): 313–31.
- ↑ Spiller, K.; Ball, K; Bandara, A. et al. (2017). "Data Privacy: Users’ Thoughts on Quantified Self Personal Data". In Ajana, B.. Self-Tracking. Palgrave Macmillan, Cham. pp. 111–24. doi:10.1007/978-3-319-65379-2_8. ISBN 9783319653792.
- ↑ Hettig, M.; Kiss, E.; Jassel, J.-F. et al. (2013). "Visualizing Risk by Example: Demonstrating Threats Arising From Android Apps". Symposium on Usable Privacy and Security (SOUPS) 2013: 1-2. https://cups.cs.cmu.edu/soups/2013/risk/paper.pdf.
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.
