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Fully Homomorphic Encryption (FHE): a Framework for Enhancing Cloud Storage Security with AES

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Fully Homomorphic Encryption (FHE): a Framework for Enhancing Cloud Storage Security with AES INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020 ISSN 2277-8616 Fully Homomorphic Encryption (FHE): A Framework For Enhancing Cloud Storage Security With AES Rudragoud Patil, R. H. Goudar Abstract: Cloud computing is an environment where a huge amount of data and programs can be stored, which are accessed through the internet on- demand. With this rapid evolvement, there are more concerns with respect to cloud technology, data security and there is a necessary requirement to enhance security algorithms that are used in the process. Homomorphic Encryption is the encryption algorithm that works on ciphertext data to provide data confidentiality. But performing the Homomorphic encryption (computations on encrypted cloud data) on a single node or in the sequential process took the more processing time and memory than the performing the same operations on the plain text (unencrypted data). Parallel processing enables us to perform operations on multiple nodes it will take lesser time to complete the applied operation than the sequential process. In this work, we also show another work on the Data partitioning method is used to improve the security of client data on the cloud. Client data will be divided into multiple parts of chunks with equal size and store on a different server. In this paper, Fully Homomorphic Encryption (FHE) framework using an Advanced Encryption Standard (AES) is implemented. It will perform various operations on ciphertext information. The implemented solution also solves the issue of noise which is coming out because of the usage of FHE on the huge cipher text. Index Terms: cloud storage, data confidentiality, data privacy and security, data partitioning, fully homomorphic encryption, gentry’s encryption algorithm. —————————— —————————— 1 INTRODUCTION That means performing the operations on the data (ciphertext) Cloud computing technology hosts the different types of that is encrypted and stored on the cloud without decrypting it services like software, hardware, networking capabilities, etc. (without converting the ciphertext into plain text). The result and provides these cloud services to the users, clients, produced by the Homomorphic encryption is the same as the organizations, public and etc., on-demand in as pay-as-you-go result produced by performing the same operations on method. In cloud computing security and privacy is a major unencrypted (plain text) data. concern. Commonly data encryption techniques are used by clients to secure the data on the cloud. Encryption techniques effectively secure the client data on the public environment called cloud computing. The client can use encryption algorithms on plaintext for security purposes before outsourcing data on the cloud, and the client can use the decryption method to get his own data from the cloud storage. Generally, if the client wants to apply some computational operations on his personal data stored on cloud storage. First, .Working of Homomorphic Encryption he should retrieve the data by decrypting the cipher text (i.e., Fig.1 converting cipher text into plain text) from the cloud. After decryption, he can apply the computing operations on that Homomorphic Encryption techniques are classified into three data, after applying the operations client can again encrypt the categories those are: Partially Homomorphic Encryption result and store it on the cloud. This decrypting the data and (PHE), Somewhat Homomorphic Encryption (SHE) and Fully applying operations, again encrypting the result is an Homomorphic Encryption (FHE) schemes. PHE scheme is the overhead procedure. So this long procedure is reduced by only method that allows performing any one operation at a using the Homomorphic encryption method. time on encrypted data. SHE scheme allows us to perform more than one operation on cipher text data but still, there is a 1.1 Homomorphic Encryption restriction on the number of multiplication and addition The Homomorphic encryption method provides an ability to operations on encrypted data. FHE scheme it supports to apply addition, multiplication and other operations on the perform any number of arithmetic operations and can also cipher text data. compute any functions. 1.2 Data Partitioning And Encryption Technique In the multi-cloud system, cloud storage is used for storing the ______________________________ user’s huge volume of data. User’s huge data can be stored on cloud storage and also users can share and download the • Rudragoud Patil, Research Scholar, VTU RRC, Department of data. As we know two major concerns like security and privacy CSE, KLS GIT, Visvesvaraya Technological University, Belagavi, cloud storage. There are many techniques that exist to provide India. E-mail: [email protected] security for user data in the cloud. Sometimes user’s data may • R. H. Goudar, Associate Professor, Department of CSE, Visvesvaraya Technological University, Belagavi, India. E-mail: lose on cloud storage. Here we present the data partitioning [email protected] method to enhance the security and privacy of user’s data. In this method, the data is first partitioned into multiple parts based on size (with equal size of chunks), after partitioning the 3728 IJSTR©2020 www.ijstr.org INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020 ISSN 2277-8616 user’s data (text file) to store them on the different cloud Homomorphic encryption is performed on the multiple nodes servers and also generates a key to store and retrieve the to reduce the processing time. This work is done on a private user's data. This method gives more security to the user’s cloud using the gentry’s algorithm. In paper [2] shows user data. If attackers get anyone chunk of the file, it’s impossible to stores their data on the cloud and they want to protect those get whole file data because the other chunks of data are data from the third party attacker or unauthorized users. So stored on different servers. Fig.2 shows the architecture users need security to their data that is stored on the cloud. diagram of data partitioning and storing them on different cloud On cloud storage, security and privacy are major concerns. servers. This paper is organized as follows in Section 2. There are several encryption methods are exist, used to Objective of the work. In Section 3 related literature work is secure the user’s data that is stored on the cloud. Some presented. Section 4 gives detail design and implementation of methods are like Full Disk Encryption and Fully Homomorphic proposed scheme. Section 5 outlines results and analysis of Encryption. Author’s presented work on Homomorphic the proposed work. Finally conclusion is presented in Section encryption and they used the Diffie Hellman algorithm for 6. symmetric key agreement. Diffie Hellman algorithm is a key exchange algorithm. When two authorized parties want to communicate with each other, this algorithm creates a session key between them. And it also creates HMAC for the user’s data integrity and ―One Time Password‖ for more security. In paper [3] cloud computing provides the on-demand services to the users of the cloud. Users are charged as per the pay-per- use model. This work based on the homomorphic encryption technique to secure client data. And they also show performing the arithmetic operations (addition and multiplication) on encrypted data. RSA algorithm is used to processes the multiplication computation on encrypted cloud data because Fig. 2. Data Partition architecture diagram. RSA is multiplicative Homomorphic encryption. Paillier encryption is used to apply Homomorphic addition operation 2 OBJECTIVE on encrypted data. In [4] this paper presents a medical application. They used the Homomorphic encryption technique In cloud computing environment Fully Homomorphic to allow computation on encrypted cloud data without encryption enables users to perform the operation on decrypting the cipher text. And also they describe encrypted cloud data. This fully Homomorphic encryption Homomorphic encryption roles on encrypted data; it will provides data confidentiality and data privacy for client data provide privacy data sharing and confidentiality of data on the that is stored on cloud storage. FHE takes more processing cloud environment. In this, they show partial Homomorphic time and memory to process the applied operations on algorithms to perform arithmetic operations on encrypted cloud encrypted cloud data than a similar operation on the data. This proposed medical operation is used to process the unencrypted data. By taking parallel processing on encrypted sensitive patient's data that is stored on the cloud. In paper [5] cloud data it will reduce the processing time in cloud they focus on storing encrypted data on the cloud using Fully computing. This work presents secure parallel processing on Homomorphic encryption. The encrypted data is stored on the encrypted cloud data using FHE. This work is done by using Database of AWS public cloud. In this public cloud, the user's Gentry’s Homomorphic encryption algorithm using the AES computations are processed on the encrypted data. A client algorithm. Here processing time is measured by the time taken can download the results from the cloud on a client machine. to execute the applied operations in parallel (on multiple The public cloud has the user’s data in cipher text. In this work nodes) and time taken to transfer the data. The main first, they create a DynamoDB instance on AWS and after disadvantage of cloud storage is security. Clients store their creating an instance, next they create Database Tables. Two important personal large volumes of data on other third-party tables are created on Dynamo Data Base. Balance is stored cloud service providers, but this stored data is not completely using the operation of the Homomorphic encryption technique. safe because many data attackers or hackers try to read this Here the user has the ability to perform subtraction and stored data. So here another goal is to build an application to addition operations on this encrypted balance.
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