Cloud-assisted Internet Of Things Research Articles

Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning

<abstract> <p>Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.</p> </abstract>

Resource-efficient workload task scheduling for cloud-assisted internet of things environment

<span lang="EN-US">One of the most challenging tasks in the internet of things-cloud-based environment is the resource allocation for the tasks. The cloud provides various resources such as virtual machines, computational cores, networks, and other resources for the execution of the various tasks of the internet of things (IoT). Moreover, some methods are used for executing IoT tasks using an optimal resource management system but these methods are not efficient. Hence, in this research, we present a resource-efficient workload task scheduling (RWTS) model for a cloud-assisted IoT environment to execute the IoT task which utilizes few numbers of resources to bring a good tradeoff, achieve high performance using fewer resources of the cloud, compute the number of resources required for the execution of the IoT task such as bandwidth and computational core. Furthermore, this model mainly focuses to reduce energy consumption and also provides a task scheduling model to schedule the IoT tasks in an IoT-cloud-based environment. The experimentation has been done using the Montage workflow and the results have been obtained in terms of execution time, power sum, average power, and energy consumption. When compared with the existing model, the RWTS model performs better when the size of the tasks is increased.</span>

Low-light aware framework for human activity recognition via optimized dual stream parallel network

Human Activity Recognition (HAR) plays a crucial role in communication and the Internet of Things (IoT), by enabling vision sensors to understand and respond to human behavior more intelligently and efficiently. Existing deep learning models are complex to deal with the low illumination, diverse viewpoints, and cluttered backgrounds, which require substantial computing resources and are not appropriate for edge devices. Furthermore, without an effective video analysis technique it processes entire frames, resulting inadequate performance. To address these key challenges, a cloud-assisted IoT computing framework is proposed for HAR in uncertain low-lighting environments, which is mainly composed of two tiers: edge and cloud computing. Initially, a lightweight Convolutional Neural Network (CNN) model is developed which is responsible to enhance the low-light frames, followed by the human detection algorithm to process the selective frames, thus enabling efficient resource utilization. Next, these refined frames are then transmitted to the cloud for accurate HAR, where dual stream CNN and transformer fusion network extract both short-and long-range spatiotemporal discriminative features followed by proposed Optimized Parallel Sequential Temporal Network (OPSTN) with squeeze and excitation attention to efficiently learn HAR in complex scenarios. Finally, extensive experiments are conducted over three challenging HAR datasets to deeply examine the proposed framework from various perspectives such as complex activity recognition, lowlighting, etc., where the results are outperformed compared with the state-of-art methods.

Lightweight Authentication Scheme for Data Dissemination in Cloud-Assisted Healthcare IoT

Recent advancements in the Internet of Things (IoT) and cloud computing technologies have accelerated the development of various practical applications, including healthcare systems. Adequately revealing the collected healthcare data in a cloud-assisted healthcare IoT system brings a huge potential for improving the safety, quality, and efficiency of healthcare services. However, often the data collected in a healthcare IoT system is vital and sensitive. The dissemination of such data is also vulnerable to malicious attacks such as tampering, eavesdropping, and forgery. Thus, disseminated data's integrity, authenticity, and privacy are elementary security demands to end-users and owners. Also, the resource-constrained nature of healthcare IoT devices invalidates the existing solutions. To address the above challenges, we propose a lightweight and secure redactable signature scheme with coarse-grained additional redaction control (CRS) for secure dissemination of healthcare data in a cloud-assisted healthcare IoT system. The security analysis indicates our CRS is secure against signature forgery, additional redaction attacks, and redacted version linkability. Compared to other existing solutions, our scheme can achieve some level of security but less computational complexity and communication overhead.

HealthDote: A blockchain-based model for continuous health monitoring using interplanetary file system

The Healthcare industry demands increased privacy and security to protect confidential patient information and comply with regulations. Both these features can be incorporated into the existing systems using Blockchain technology. The only challenge faced here is the ease of users, but this can be quickly resolved by integrating the Internet of Things (IoT) and blockchain. IoT-based devices overcome limited computing capacity for personal intelligent health devices. Cloud-assisted IoT devices also require limited storage capacity for devices like wearable sensors. However, it must be considered that this system still has drawbacks, leading to its inefficiency. These problems include Data Privacy and Data sharing. This paper proposes an Interplanetary File System (IPFS) based solution to these problems. Here, the users continually upload the health data collected by IoT devices and add them to blockchain transactions that the other user nodes, such as physicians, pharmacists, insurance companies, hospital authorities, etc., can access. This system ensures the well-being of the users by monitoring the data gathered every 5 min and daily. It also facilitates the alarm feature in an emergency, making it reliable. The user receives daily notifications regarding his lifestyle, and the family members receive the notifications on his behalf if there are some chances of an emergency. The authorized doctors are also notified immediately in case an emergency is detected. Apart from this feature, the user can get consultations from doctors, prescriptions from the pharmacist, funds from insurance authorities, and hospital supplies, all through the transaction on the six blockchains of HealthDote using the cryptocurrency DoteCoins, which are designed specifically for this system.

An Adaptive Lightweight Hybrid Encryption Scheme for Securing the Healthcare Data in Cloud-Assisted Internet of Things

An Adaptive Lightweight Hybrid Encryption Scheme for Securing the Healthcare Data in Cloud-Assisted Internet of Things

Efficient Revocable Storage Attribute-based Encryption With Arithmetic Span Programs in Cloud-Assisted Internet of Things

Revocable storage and efficient description of the access policy are necessary to enhance the practicality of the attribute-based encryption (ABE) in real-life scenarios, such as cloud-assisted Internet of Things (IoT). Nevertheless, existing ABE works fail to balance the two vital factors. In this paper, we construct an efficient revocable storage ciphertext-policy attribute-based encryption with arithmetic span programs (RS- CPABE-ASP). The arithmetic span program (ASP) is elegantly utilized as the access structure to reduce the unnecessary cost for defining access policy. Combining the indirect revocation and the ciphertext update mechanism, our work prevents the revoked user unable to access the newly generated data and the old data that can be accessed before. As shown in the outsourced version of RS-CPABE-ASP, the costly part for users to decrypt the data can be outsourced to powerful cloud servers. In this way, users in our RS-CPABE-ASP are able to access their data in a more efficient way by merely one exponential operation. Finally, we carry out detailed theoretical analysis and experimental simulations to evaluate the performance of our work. The results fairly show that our proposed work is efficient and feasible in cloud-assisted IoT.

An Efficient Revocable and Searchable MA-ABE Scheme With Blockchain Assistance for C-IoT

Internet of Things (IoT) devices usually stores data on clouds for computational overhead offloading and easy data sharing. The data owners, as a result, usually have concerns about the security and privacy of their data stored in such cloud-assisted IoT (C-IoT) systems. Traditional encryption and search primitives, including attribute-based encryption (ABE) and public-key encryption with keyword search (PEKS), however, suffer from high overheads in decryption and revocation, and privacy leakage in search. To address these issues, we propose an efficient revocable and searchable multiauthority ABE (MA-ABE) scheme named ERS-ABE, which utilizes blockchain (BC) technology to implement keyword-based search and dynamic user management. ERS-ABE also adopts cloud-assisted decryption to improve the efficiency of IoT devices. It has been proven to be secure against the selective replayable chosen-ciphertext attacks and the chosen-keyword attacks under the random oracle model. The feasibility and efficiency of ERS-ABE have been evaluated through theoretical analysis and extensive simulation studies. The results indicate that EAR-ABE performs better over the state-of-the-art in both storage and computational overheads. Particularly, the operations that are usually done by a central server but taken by a BC in EAR-ABE cost only a few seconds.

Secure and Lightweight User Authentication Scheme for Cloud-Assisted Internet of Things

Cloud-assisted Internet of Things (IoT) overcomes the resource-constrained nature of the traditional IoT and is developing rapidly in such fields as smart grids and intelligent transportation. In a cloud-assisted IoT system, users can remotely control the IoT devices and send specific instructions to them. If the users’ identities are not verified, adversaries can pretend as legitimate users to send fake and malicious instructions to IoT devices, thereby compromising the security of the entire system. Thus, a sound authentication mechanism is indispensable to ensure security. At the same time, it should be noted that a gateway may connect to massive IoT devices with the exponential growth of interconnected devices in a cloud-assisted IoT system. The efficiency of authentication schemes is easily impacted by the computation capability of the gateway. Recently, several schemes have been designed for cloud-assisted IoT systems, but they have problems of one kind or another, making them not very suitable for cloud-assisted IoT systems. In this paper, we take a typical scheme (proposed at IEEE TDSC 2020) as an example to identify the common weaknesses and challenges of designing a user authentication scheme for cloud-assisted IoT systems. In addition, we propose a new secure user authentication scheme with lightweight computation on gateways. The proposed scheme provides secure access between remote users and IoT devices with many ideal attributions, such as forward secrecy and multi-factor security. Meanwhile, the security of this scheme is proved under the random-oracle model, heuristic analysis, the ProVerif tool and BAN logic. Compared with ten state-of-the-art schemes in security and performance, the proposed scheme achieves all the listed twelve security requirements with minimum computation and storage costs on gateways.

Privacy-Preserving Threshold-Based Image Retrieval in Cloud-Assisted Internet of Things

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ranked image retrieval solutions have low retrieval efficiency and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both ciphertext-only attack (COA) and chosen-plaintext attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world data sets.

Guest Editorial: Security and Privacy for Cloud-Assisted Internet of Things (IoT) and Smart Grid

Cloud computing has emerged as a new technological domain in the IT industry. Currently, several organizations working in various domains, such as healthcare, finance, manufacturing, smart grid, Internet of Things (IoT), and IT, are increasingly integrating cloud computing with their traditional applications. The key idea behind the usage of cloud computing in IoT is to increase efficiency without compromising the data quality. When it comes to collecting data of thousands or millions of servers, the cloud offers scalability and reduces the computational load on each sensor. The highly configured servers in the cloud are very useful in processing and analyzing the sensors’ data. The security of such a shared infrastructure is very crucial. It is the major barrier in the adoption of cloud-based services, followed by issues regarding compliance, privacy, and legal matters. The digitalization of critical infrastructures, such as smart grids (SGs), brings advantages and opportunities for remote access and control. It enables intelligent and online monitoring of these systems, which considerably enhances cyberattacks’ vulnerability. Cyberattacks are among the most important threats to SGs. Therefore, efficient control systems should be designed that can detect and isolate cyberattacks to keep the SG reliable and secure operation. This special section aims at providing a forum to discuss the most recent advances on security and privacy in cloud-assisted IoT and SG applications.

A Privacy-Enhanced Retrieval Technology for the Cloud-Assisted Internet of Things

In the cloud-assisted Internet of things (IoT), most of the data are sent to the cloud for storage and processing. Data privacy and security are extreme concerns since retrieving data from the cloud will yield privacy disclosure risk due to the cloud’s openness. To this end, this article proposes PERT, a privacy-enhanced retrieval technology for cloud-assisted IoT. This architecture is designed through an implicit index maintained by edge servers and a hierarchical retrieval model that preserves data privacy by hiding the information of data transmission between the cloud and the edge servers. For the hierarchical retrieval model, we designed a data partition strategy. The edge server stores partial data. In this way, data privacy is preserved since the attacker must get the data maintained by both cloud and edge servers. The detailed performance analysis and extensive experiments have displayed the effectiveness of the technology for data privacy. It is tested that the architecture can efficiently and securely retrieve the stored data while the computation cost is reduced through operation downsizing. Compared with the benchmark cloud encrypted storage model, the time cost of this method is significantly reduced when the number of users is relatively large.

An efficient and access policy-hiding keyword search and data sharing scheme in cloud-assisted IoT

By leveraging the cloud computing paradigm, the cloud-assisted Internet of Things (IoT) is able to offer the massive storage and highly available computation services for end-users. When the data collected from IoT devices is endlessly gathering to the cloud center, the data security become new challenges. Encrypting data is an effective measure to guarantee data confidentiality. However, traditional encryption techniques make the data searching and access control inoperative. Current attribute-based keyword search (ABKS) techniques provide a feasibility to achieve data searching and access control over encrypted data simultaneously. However, existing ABKS schemes leak sensitive information via clear access policy and may be unsuitable for certain IoT applications, where the access policies in data might contain private information of data owners such as the security number or the residential area of a resident in smart grids. In this work, we design an efficient and policy-hiding attribute-based keyword search and data sharing scheme (PH-ABKS-DS) in cloud-assisted IoT. To the best of our knowledge, this construction is the first PH-ABKS-DS with practical efficiency that is constructed on prime order group. We provide detailed correctness and security analyses for PH-ABKS-DS. Extensive experiments demonstrate that our proposed PH-ABKS-DS is correct and practical.

Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.

Enabling secure mutual authentication and storage checking in cloud-assisted IoT.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.

A Survey of Public-Key Encryption With Search Functionality for Cloud-Assisted IoT

Nowadays, Internet of Things (IoT) is an attractive system to provide broad connectivity of a wide range of applications, and clouds are natural promoters. Cloud-assisted IoT combines the advantages of cloud computing and IoT, which is able to collect data from the real world and maximizes the value of the collected data by the means of data sharing and data analysis. Meanwhile, secure and convenient data retrieval in cloud servers becomes an important requirement for both enterprises and individual users. Public-key encryption with search functionality (shorten as PKE-SF) is a widely used cryptographic technique that allows users to retrieve encrypted data without decryption. PKE-SF mainly contains the primitives of public-key encryption with keyword search (PKE-KS), public-key encryption with equality test (PKE-ET), and plaintext-checkable encryption (PCE). In light of the overwhelming variety and multitude of PKE-SF schemes, this survey presents these schemes from different perspectives to provide better comprehension for beginners and advanced researchers. More concretely, this survey concentrates on the state of the art of PKE-SF by analyzing the design rationale, examining the framework and security model, and assessing the existing schemes in accordance with theoretic efficiency, security properties, and experimental performance. Furthermore, we discuss the extensions of traditional PKE-SF schemes which feature with the access control delegation, conjunctive keyword search, certificate-free, and offline keyword guessing attack resilience. Finally, we point out some promising directions for readers.

Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

PurposeLimitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.Design/methodology/approachIn this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.FindingsThe newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.Research limitations/implicationsIn this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.Practical implicationsThe developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.Social implicationsThe social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.Originality/valueIn this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

Fair multi-owner search over encrypted data with forward and backward privacy in cloud-assisted Internet of Things

The trend towards the Internet of Things (IoT) technology promises the growth of home and organizational automation in the world. Due to the large amount of IoT data stored in the semi-trust cloud server, the IoT is vulnerable to cyberattacks. Therefore, the privacy violation of outsourced data is an obstacle to efficient IoT data storage. Searchable encryption is a promising solution for secure storage and selective data retrieval. However, achieving desired privacy, performance, and attributes such as dynamics, access control, scalability, and fair arbitration is still a challenge in searchable encryption schemes. In this paper, we propose a fair and dynamic multi-owner searchable encryption scheme wherein dynamics does not leak the sensitive information. In the proposed scheme, the verifiability and fair arbitration, respectively, prevent the malicious cloud servers and malicious users from having any motivation to disrupt the accuracy of the exchanged data. Generating a single trapdoor corresponding to the conjunctive keyword query, a client can search over all documents stored in the cloud, the owner of which has allowed the client to access. We also prove that our scheme is secure against the keyword guessing attack in the standard model. Finally, evaluating the performance of the proposed scheme on a real dataset, we demonstrate its efficiency as well.

Privacy-Preserving Attribute-Based Keyword Search with Traceability and Revocation for Cloud-Assisted IoT

With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.

Identity-based outsider anonymous cloud data outsourcing with simultaneous individual transmission for IoT environment

The integration of the Internet of Things (IoT) and cloud computing has become an attractive cloud-oriented big data processing paradigm, which is playing an important role in efficiency and productivity for digitalization of numerous IoT enabled industries. However, cloud-assisted IoT is also becoming an increasingly attractive target for various cyber-attacks, including the authenticity of outsourcing data, untrustworthiness of third parties, and data security and privacy. As a potential and promising solution to securely outsource data, we present the first construction for an efficient cloud data outsourcing system with simultaneous individual transmission preserving outsider anonymity of the subscribed consumer set. In our system, a data owner generates personalized data for each of the consumers in a group and transmits a common encrypted data for the group in such a way that the subscribed consumer set is completely hidden from the outsiders. Personalized data can be recovered only by an authorized consumer, while the common data can be decrypted by all the authorized consumer in that group. The communication bandwidth is compact in our construction, and the decryption algorithm requires significantly less computation cost. We design our scheme using asymmetric bilinear map over the prime order group to prevent fault attacks on symmetric bilinear map. Our construction is built in identity-based setting without any non-standard q-type security assumption and does not use random oracles. Our scheme enjoys adaptive security against an indistinguishable chosen-plaintext attack under the hardness of the standard decisional bilinear Diffie–Hellman exponent problem. Furthermore, our design supports an exponential number of consumers as the size of the valid identity set grows exponentially with the security parameter, whereas it is only polynomial in the security parameter for the existing cloud data outsourcing systems. In particular, the implementation and performance analysis explicates the advantages of our design for resource-constrained IoT enabled frameworks.