Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

© 2013 IEEE. Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations\u27 data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication - read file, second-level authentication - download file, and third-level authentication - download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches

Internet of Thing Based Confidential Healthcare Data Storage, Access Control and Monitoring Using Blockchain Technique

Internet of Things plays a significant role in multiple sectors like agriculture, manufacturing and healthcare for collecting information to automation. The collected information is in different diversity and consists of confidential and non-confidential information. Secure handling of confidential data is a crucial task in cloud computing like storage, access control and monitoring. The blockchain based storage technique provides immutable data storage, efficient access control and dynamic monitoring to confidential data. Thus, the secure internet of things data storage, access control and monitoring using blockchain technique is proposed in this work. The patients health information that are in different formats are pruned by a decision tree algorithm and it classifies the confidential data and non-confidential data by the fuzzy rule classification technique. Depending on data owner's willing, the fuzzy rule is framed and the confidential and non-confidential data collected by internet of things sensors are classified. To provide confidentiality to confidential data, Attribute Based Encryption is applied to confidential data and stored in an off-chain mode of blockchain instead of entire data encryption and storage. The non-confidential data is stored in a plaintext form in cloud storage. When compared to support vector machine, K-nearest neighbor and Naive Bayes classification techniques, the proposed fuzzy rule based confidential data identification produces greater than 96 % of accuracy based on data owner willing and confidential data storage takes lesser than 20 % of storage space and processing time in an entire data storage. Additionally, the blockchain performances like throughput, network scalability and latency is optimized through minimal block size and transactions. Thus, our experimental results show that the proposed blockchain based internet of things data storage, access control and monitoring technique provides better confidentiality and access control to confidential data than the conventional cloud storage technique with lesser processing time

Secure monitoring system for industrial internet of things using searchable encryption, access control and machine learning

This thesis is an alternative format submission comprising a set of publications and a comprehensive literature review, an introduction, and a conclusion. Continuous compliance with data protection legislation on many levels in the Industrial Internet of Things (IIoT) is a significant challenge. Automated continuous compliance should also consider adaptable security compliance management for multiple users. The IIoT should automate compliance with corporate rules, regulations, and regulatory frameworks for industrial applications. Thus, this thesis aims to improve continuous compliance by introducing an edge-server architecture which incorporates searchable encryption with multi-authority access to provide access to useful data for various stakeholders in the compliance domain. In this thesis, we propose an edge lightweight searchable attribute-based encryption system (ELSA). The ELSA system leverages cloud-edge architecture to improve search time beyond a previous state-ofthe-art encryption solution. The main contributions of the first paper are as follows. First, we npresent an untrusted cloud and trusted edge architecture that processes data efficiently and optimises decision-making in the IIoT context. Second, we enhanced the search performance over the current state-of-the-art (LSABE-MA) regarding order of magnitude. We achieved this enhancement by storing keywords only on the trusted edge server and introducing a query optimiser to achieve better-than-linear search performance. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for a linear search. As a result, we achieved higher performance without sacrificing result accuracy. In the second paper, we extended ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and enables the record to be returned with single keyword queries. In addition, the experiments demonstrated the scalability and efficiency of ELSA with an increasing number of keywords and complexity. Based on the experimental results and feedback received from the publication and presentation of this work, we published our third technical paper. In this paper, we improved ELSA by minimising the lookup table size and summarising the data records by integrating machine-learning (ML) methods suitable for execution at the edge. This integration removes records of unnecessary data by evaluating added value to further processing. This process results in the minimisation of the lookup table size, the cloud storage, and the network traffic, taking full advantage of the edge architecture benefits. We demonstrated the mini-ELSA expanded method on two well-known IIoT datasets. Our results reveal a reduction of storage requirements by > 21% while improving execution time by > 1.39× and search time by > 50% and maintaining an optimal balance between prediction accuracy and space reduction. In addition, we present the computational complexity analysis that reinforces these experimental results

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

Abstract: Please refer to full text to view abstrac

Do not tell me what I cannot do! (The constrained device shouted under the cover of the fog): Implementing Symmetric Searchable Encryption on Constrained Devices (Extended Version)

Symmetric Searchable Encryption (SSE) allows the outsourcing of encrypted data to possible untrusted third party services while simultaneously giving the opportunity to users to search over the encrypted data in a secure and privacy-preserving way. Currently, the majority of SSE schemes have been designed to fit a typical cloud service scenario where users (clients) encrypt their data locally and upload them securely to a remote location. While this scenario fits squarely the cloud paradigm, it cannot apply to the emerging field of Internet of Things (IoT). This is due to the fact that the performance of most of the existing SSE schemes has been tested using powerful machines and not the constrained devices used in IoT services. The focus of this paper is to prove that SSE schemes can, under certain circumstances, work on constrained devices and eventually be adopted by IoT services. To this end, we designed and implemented a forward private dynamic SSE scheme that can run smoothly on resource-constrained devices. To do so, we adopted a fog node scenario where edge (constrained) devices sense data, encrypt them locally and use the capabilities of fog nodes to store sensed data in a remote location (the cloud). Consequently, end users can search for specific keywords over the stored ciphertexts without revealing anything about their content. Our scheme achieves efficient computational operations and supports the multi-client model. The performance of the scheme is evaluated by conducting extensive experiments. Finally, the security of the scheme is proven through a theoretical analysis that considers the existence of a malicious adversary

Blockchain-aided privacy-preserving medical data sharing scheme for e-healthcare system

Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multi-keyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files, but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multi-keyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks

Fog computing security and privacy issues, open challenges, and blockchain solution: An overview

Due to the expansion growth of the IoT devices, Fog computing was proposed to enhance the low latency IoT applications and meet the distribution nature of these devices. However, Fog computing was criticized for several privacy and security vulnerabilities. This paper aims to identify and discuss the security challenges for Fog computing. It also discusses blockchain technology as a complementary mechanism associated with Fog computing to mitigate the impact of these issues. The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing

Footsteps in the fog: Certificateless fog-based access control

The proliferating adoption of the Internet of Things (IoT) paradigm has fuelled the need for more efficient and resilient access control solutions that aim to prevent unauthorized resource access. The majority of existing works in this field follow either a centralized approach (i.e. cloud-based) or an architecture where the IoT devices are responsible for all decision-making functions. Furthermore, the resource-constrained nature of most IoT devices make securing the communication between these devices and the cloud using standard cryptographic solutions difficult. In this paper, we propose a distributed access control architecture where the core components are distributed between fog nodes and the cloud. To facilitate secure communication, our architecture utilizes a Certificateless Hybrid Signcryption scheme without pairing. We prove the effectiveness of our approach by providing a comparative analysis of its performance in comparison to the commonly used cloud-based centralized architectures. Our implementation uses Azure – an existing commercial platform, and Keycloak – an open-source platform, to demonstrate the real-world applicability. Additionally, we measure the performance of the adopted encryption scheme on two types of resource-constrained devices to further emphasize the applicability of the proposed architecture. Finally, the experimental results are coupled with a theoretical analysis that proves the security of our approach