Abstract
Cloud computing is like a daily routine now a day. Even though it has numbers of advantages in technical and business view, still there are some challenges there like data storage security, confidentiality and integrity. Main risk in cloud data is about to trust on cloud owner. Encrypted data is not useful for any computational process, so we cannot store as encrypted data. In recommendation system cloud plays very important role. Using homomorphic encryption, we can perform cloud data analyzation. This paper discusses about different homomorphic encryption technique and solution to recommendation system.
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Keywords
- Cloud data storage
- Homomorphic encryption
- Data security
- Data confidentiality
- Data integrity
- Recommendation system
- Collaborative filtering
1 Introduction
Cloud computing is a most popular architectural model in the field of Information Technology. It is combination of Distributed Computing, Parallel Computing and Grid Computing Architectures. It provides following kinds of services: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). Basic Computing Resources and Storage Network Services can be categorized under IaaS. PaaS provide service to develop and run application without any worry about its complexity and maintenance. SaaS is a service in which we can provide features like subscribe and software licensing. For all these services, there is no need for users to manage or control the cloud infrastructure, including network, server, operating system (OS), storage and even the functions of applications [1, 2]. In other words, we can say cloud computing is a third-party service which can be used for delivery of the applications [3]. Some well-known service providers like Rack space, Microsoft, IBM. The buzz ‘cloud computing’ word way back in 2006 with the launch of Amazon EC2, gained traction in 2007 [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29].
The research paper is divided into various sections. Section 2 introduce Recommendation system & literature review followed by Collaborative filtering and Homomorphic encryption in Sect. 3. Section 4 contains the proposed work and in last we will try to conclude the study and its future scope.
2 Recommendation System
Recommendation system (RS) is a one kind of information filtering system that leads to predict some information regarding products, items or preferences [5–9]. RS became very popular in recent years and useful in many areas like movie, music, news, books, social tags, research articles, search queries and products in general. Another popular RS are restaurant, life insurance, online dating, and Twitter pages.
Figure 1 Shows overview of RS. Based on user past history and rating, system will try to match them. After that it will recommend some information. Recommendation system can be divided into two technique: Profile Based and Collaborative Filtering (CF) [4].
Recommendation system
CF can further divide into two types: Item based CF and User Based CF. Item based CF will suggest items based on user’s previous preferences. In User based CF, system will suggest items based on other user’s activity who have similar kind of preferences.
As per research paper study and observation from theory we would like to share some views on homomorphic encryption that there are numbers of homomorphic algorithms are available. Among them we can choose any algorithm as per our requirement. For example, we require lightweight encryption to reduce complexity and power consumption specially in Internet of Things (IOT) application, we can use partially additive HE. For better understanding consider Table 1 which shows comparisons of some homomorphic encryption and its applications.
As Shown in Table 1, many algorithms are available but only BGV can be implemented on Cloud storage. As per our recommendation point of view we will choose BGV algorithm.
3 Homomorphic Encryption
Homomorphic Encryption (HE) is a one kind of encryption that allow to process on encrypted data. In HE we first need to encrypt data using secret encryption key. After processing on data we need to use decryption key to get original data. Advantage of HE is that after processing on encrypted data we can get the same result as it was applied on plain text or original data.
Advantage of homomorphic encryption over simple encryption is we do not need to worry about data integrity and privacy. In Fig. 2 we had shown simple mechanism of homomorphic encryption. Homomorphic encryption has major two operations: addition and multiplication. According to operation performed on the data it can be classified into mainly two sub categories. One is fully homomorphic encryption and another is partially homomorphic encryption. In fully homomorphic encryption both addition and multiplication is performed, while in partially homomorphic encryption any of them is used.
Homomorphic encryption
4 Proposed Work
To Ensure data privacy we need to encrypt data. For that we proposed a better encryption scheme over simple encryption and it is HE. As we know that recommendation system will recommend items from stored data in cloud. So before putting data on cloud encrypt them using BGV algorithm. If we are supposed to share that information with third party’s recommendation system, there will be no issue of data privacy and data integrity.
Figure 3 shows basic diagram of proposed system. From the figure it’s clear that it will solve issue of data privacy, data confidentiality and data integrity.
Proposed homomorphic encryption for recommendation system
5 Conclusion
Providing security in cloud storage is a biggest challenge. Here in we presented our views and a way to provide data privacy and security to cloud data and it can be used for further process like recommender system. In this paper, we proposed a demo level of mechanism of homeomorphism encryption on recommender system, but in real time it require high computational power as cloud has big data storage. In future work, we will try to provide a solution with better efficiency so that it can deal with big amount of data.
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Soni, K., Panchal, G. (2018). Data Security in Recommendation System Using Homomorphic Encryption. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_37
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DOI: https://doi.org/10.1007/978-3-319-63673-3_37
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