Privacy-preserving data search with fine-grained dynamic search right management in fog-assisted Internet of Things

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Fog computing, as an assisted method for cloud computing, collects Internet of Things (IoT) data to multiple fog nodes on the edge of IoT and outsources them to the cloud for data search, and it reduces the computation cost on IoT nodes and provides fine-grained search right management. However, to provide privacy-preserving IoT data search, the existing searchable encryptions are very inefficient as the computation cost is too high for the resource-constrained IoT ends. Moreover, to provide dynamic search right management, the users need to be online all the time in the existing schemes, which is impractical. In this paper, we first present a new fog-assisted privacy-preserving IoT data search framework, where the data from each IoT device is collected by a fog node, stored in a determined document and outsourced to the cloud, the users search the data through the fog nodes, and the fine-grained search right management is maintained at document level. Under this framework, two searchable encryption schemes are proposed, i.e., Credible Fog Nodes assisted Searchable Encryption (CFN-SE) and Semi-trusted Fog Nodes assisted Searchable Encryption (STFN-SE). In CFN-SE scheme, the indexes and trapdoors are generated by the fog nodes, which greatly reduce the computation costs at the IoT devices and user ends, and fog nodes are used to support offline users’ key update. In STFN-SE scheme, the semi-trusted fog nodes are used to provide storage of encrypted key update information to assist offline users’ search right update. In both schemes, no re-encryption of the keywords is needed in search right updates. The performance evaluations of our schemes demonstrate the feasibility and high efficiency of our system.National Key Research and Development ProgramNational Natural Science Foundation of ChinaSichuan Provincial Major Frontier IssuesState Key Laboratory of Integrated Services Networks, Xidian Universit

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

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

Security and Privacy in the Internet of Things

The Internet of Things (IoT) is an emerging paradigm that seamlessly integrates electronic devices with sensing and computing capability into the Internet to achieve intelligent processing and optimized controlling. In a connected world built through IoT, where interconnected devices are extending to every facet of our lives, including our homes, offices, utility infrastructures and even our bodies, we are able to do things in a way that we never before imagined. However, as IoT redefines the possibilities in environment, society and economy, creating tremendous benefits, significant security and privacy concerns arise such as personal information confidentiality, and secure communication and computation. Theoretically, when everything is connected, everything is at risk. The ubiquity of connected things gives adversaries more attack vectors and more possibilities, and thus more catastrophic consequences by cybercrimes. Therefore, it is very critical to move fast to address these rising security and privacy concerns in IoT systems before severe disasters happen. In this dissertation, we mainly address the challenges in two domains: (1) how to protect IoT devices against cyberattacks; (2) how to protect sensitive data during storage, dissemination and utilization for IoT applications. In the first part, we present how to leverage anonymous communication techniques, particularly Tor, to protect the security of IoT devices. We first propose two schemes to enhance the security of smart home by integrating Tor hidden services into IoT gateway for users with performance preference. Then, we propose a multipath-routing based architecture for Tor hidden services to enhance its resistance against traffic analysis attacks, and thus improving the protection for smart home users who desire very strong security but care less about performance. In the second part of this dissertation, we explore the solutions to protect the data for IoT applications. First, we present a reliable, searchable and privacy-preserving e-healthcare system, which takes advantage of emerging cloud storage and IoT infrastructure and enables healthcare service providers (HSPs) to realize remote patient monitoring in a secure and regulatory compliant manner. Then, we turn our attention to the data analysis in IoT applications, which is one of the core components of IoT applications. We propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with machine learning service providers (MLSPs) and conduct computation intensive classification on behalf of the resourced-constrained IoT devices in a privacy-preserving manner. Finally, we explore the problem of privacy-preserving targeted broadcast in IoT, and propose two multi-cloud-based outsourced-ABE (attribute-based encryption) schemes. They enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing attributes from being disclosed

BLA2C2: Design of a Novel Blockchain-based Light-Weight Authentication & Access Control Layer for Cloud Deployments

Cloud deployments are consistently under attack, from both internal and external adversaries. These attacks include, but are not limited to brute force, masquerading, improper access, session hijacking, cross site scripting (XSS), etc. To mitigate these attacks, a wide variety of authentication & access control models are proposed by researchers, and each of them vary in terms of their internal implementation characteristics. It was observed that these models are either highly complex, or lack in terms of security under multiple attacks, which limits their applicability for real-time deployments. Moreover, some of these models are not flexible and cannot be deployed under dynamic cloud scenarios (like constant reconfigurations of Virtual Machines, dynamic authentication use-cases, etc.). To overcome these issues, this text proposes design of a novel blockchain-based Light-weight authentication & access control layer that can be used for dynamic cloud deployments. The proposed model initially applies a header-level light-weight sanitization layer that removes Cross Site Scripting, SQL Injection, and other data-level attacks. This is followed by a light-weight authentication layer, that assists in improving login-level security for external attacks. The authentication layer uses IP matching with reverse geolocation mapping in order to estimate outlier login attempts. This layer is cascaded with an efficient blockchain-based access control model, which assists in mitigating session hijacking, masquerading, sybil and other control-level attacks. The blockchain model is developed via integration of Grey Wolf Optimization (GWO) to reduce unnecessary complexities, and provides faster response when compared with existing blockchain-based security deployments. Efficiency of the model was estimated in terms of accuracy of detection for different attack types, delay needed for detection of these attacks, and computational complexity during attack mitigation operations. This performance was compared with existing models, and it was observed that the proposed model showcases 8.3% higher accuracy, with 10.5% lower delay, and 5.9% lower complexity w.r.t. standard blockchain-based & other security models. Due to these enhancements, the proposed model was capable of deployment for a wide variety of large-scale scenarios

Using Attribute-Based Access Control, Efficient Data Access in the Cloud with Authorized Search

The security and privacy issues regarding outsourcing data have risen significantly as cloud computing has grown in demand. Consequently, since data management has been delegated to an untrusted cloud server in the data outsourcing phase, data access control has been identified as a major problem in cloud storage systems. To overcome this problem, in this paper, the access control of cloud storage using an Attribute-Based Access Control (ABAC) approach is utilized. First, the data must be stored in the cloud and security must be strong for the user to access the data. This model takes into consideration some of the attributes of the cloud data stored in the authentication process that the database uses to maintain data around the recorded collections with the user\u27s saved keys. The clusters have registry message permission codes, usernames, and group names, each with its own set of benefits. In advance, the data should be encrypted and transferred to the service provider as it establishes that the data is still secure. But in some cases, the supplier\u27s security measures are disrupting. This result analysis the various parameters such as encryption time, decryption time, key generation time, and also time consumption. In cloud storage, the access control may verify the various existing method such as Ciphertext Policy Attribute-Based Encryption (CP-ABE) and Nth Truncated Ring Units (NTRU). The encryption time is 15% decreased by NTRU and 31% reduced by CP-ABE. The decryption time of the proposed method is 7.64% and 14% reduced by the existing method

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

Efficient and Secure Key Management and Authentication Scheme for WBSNs Using CP-ABE and Consortium Blockchain

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