Searching and Sorting of Fully Homomorphic Encrypted Data on Cloud

The challenge of maintaining confidentiality of stored data in cloud is of utmost importance to realize the potential of cloud computing. Storing data in encrypted form may solve the problem, but increases the security issues and diminishes the essence of cloud while performing operations on cloud data by repeated decryption-encryption. Hence, Fully homomorphic encryption (FHE) is an effective scheme to support arbitrary operations directly on encrypted data. Further, cloud mostly acts as storage database, hence secured sorting and searching of FHE cloud data can be an effective field of research. We have investigated the feasibility of performing comparison as well as partition based sort on CPA resistant FHE data and highlight an important observation that time requirement of partition based sort on FHE data is no better than comparison based sort owing to the security of the cryptosystem. We identify the recrypt operation, which is the denoising step of FHE as the main reason of costly timing requirement of such operations. Finally, we propose a two stage sorting technique termed as Lazy sort with reduced recrypt operation, which proves to be better in terms of performance on FHE data in comparison to partition as well as comparison sort

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan

A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholder

Cloud Storage Protection Scheme Based on Fully Homomorphic Encryption

Cloud computing allows enterprises and individuals to have a less physical infrastructure of software and hardware. Nevertheless, there are some concerns regarding privacy protection which may turn out to be a strong barrier. Traditional encryption schemes have been used to encrypt the data before sending them to the cloud. However, the private key has to be provided to the server before any calculations on the data. To solve this security problem, this paper proposes a fully homomorphic encryption scheme for securing cloud data at rest. The scheme is based on prime modular operation, its security depends on factoring multiple large prime numbers (p1, p2,...pn) up to n, which is formed from very large prime numbers up to hundreds of digits as this is an open problem in mathematics. In addition, the elements of the secret key are derived from a series of mathematical operations and the calculation of an Euler coefficient within the modular of integers. Furthermore, it adds the complexity of noise to the plaintext using the number of users of the Cloud Service Provider. Moreover, its randomness is evaluated by the National Institute of Standards and Technology statistical tests, and the results demonstrating that the best statistical performance was obtained with this algorithm