Cloud-assisted IoT Research Articles

Toward Fine-Grained and Forward-Secure Access Control in Cloud-Assisted IoT

Toward Fine-Grained and Forward-Secure Access Control in Cloud-Assisted IoT

Secure and Real-Time Traceable Data Sharing in Cloud-Assisted IoT

Secure and Real-Time Traceable Data Sharing in Cloud-Assisted IoT

Verifiable Multi-Level Dynamic Searchable Encryption With Forward and Backward Privacy in Cloud-Assisted IoT

Verifiable Multi-Level Dynamic Searchable Encryption With Forward and Backward Privacy in Cloud-Assisted IoT

Segmented Hash-Based Privacy-Preserving Image Retrieval Scheme in Cloud-Assisted IoT

Segmented Hash-Based Privacy-Preserving Image Retrieval Scheme in Cloud-Assisted IoT

Vector Sum Range Decision for Verifiable Multiuser Fuzzy Keyword Search in Cloud-Assisted IoT

Vector Sum Range Decision for Verifiable Multiuser Fuzzy Keyword Search in Cloud-Assisted IoT

Efficient Data Aggregation and Duplicate Removal using Grid-Based Hashing in cloud-assisted Industrial IoT

Efficient Data Aggregation and Duplicate Removal using Grid-Based Hashing in cloud-assisted Industrial IoT

Attribute-Based Searchable Encryption With Forward Security for Cloud-Assisted IoT

Attribute-Based Searchable Encryption With Forward Security for Cloud-Assisted IoT

Cyberattacks and Security of Cloud Computing: A Complete Guideline

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security.

Applications hosting over cloud-assisted IOT: a productivity model and method defining accessibility of data security

Applications hosting over cloud-assisted IOT: a productivity model and method defining accessibility of data security

A Secure and Efficient Scheme Based on Unlinkability and Anonymous Traceable Protocol for Cloud-Assisted IoT Environment

The authenticity, privacy, and nonrepudiation of IoT applications are all guaranteed by the ID-Based Signature (IDBS) cryptographic technique. Digital communication presents challenges for information evaluation and removal-back transmission. Security was consequently needed in a cloud-based system to interact with the correct location or nodes. Furthermore, in a cloud-based environment, it is difficult to track a group’s key escrow and secret key generations. We improve group signature security, an identity-based method, to address these problems. The strategy asks a member to sign documents on the group’s behalf. The group manager (GM) membership is controlled by two protocols: join and revoke. The GM may utilize the Open protocol to determine who signed a communication. The classic ID-Based Group Signature (IDBGS) method uses a public-key generator (PKG). This method is founded on difficult mathematical ideas like the computational Diffie–Hellman assumptions. Based on the ID-Based GS system, this technique enhances group members’ identity, legitimacy and visibility. The public key size and signature depend on the group members’ autonomy. The improved method guarantees realistic confirmation of nodes and fortifies them against forgery attempts. This solution promises faster execution and increased dependability for secure nodes.

Joint DNN Partition and Resource Allocation for Task Offloading in Edge–Cloud-Assisted IoT Environments

Multi-access Edge Computing (MEC) is a promising approach to enhancing IoT devices running AI-based services. Especially, the edge-cloud architecture acts as a strong supporter of the resource-limited IoT devices. How to optimize the system resources efficiently to improve the service performance is the key issue in this scenario. Motivated by this, in this paper, we focus on a multi-Base Station (BS) and multi-service edge-cloud-assisted IoT environment, where both the BSs (with edge servers deployed) and the cloud can assist the IoT devices to process multi-type Deep Learning (DL) tasks via task offloading. DNN partition mechanism and both the communication and computing resources allocation are utilized to enable a collaborative optimization to minimize the processing delay of all the DL tasks in the system. Due to the Mixed Integer NonLinear Programming (MINLP) characteristic of our optimization problem, we propose an algorithm that decomposes the original problem into two sub-problems, solves them separately, and then obtains the near optimal solution efficiently. Extensive simulations are conducted by varying 5 different crucial parameters. The superiority of our scheme is demonstrated in comparisons with several other schemes proposed by existing works. Our scheme can achieve a notable 28.3% delay reduction on average.

Expressive Data Sharing and Self-Controlled Fine-Grained Data Deletion in Cloud-Assisted IoT

Expressive data sharing and efficient data deletion are essential to drive the development of cloud-assisted IoT. But insecure transmission and the vulnerability of the cloud server may cause potential threats to IoT data, attribute-based encryption (ABE) is widely applied to ensure data confidentially. Nonetheless, the potential data exposure caused by the compromised long-term key and the contradiction between conventional access structures in ABE and the various demands of data owners are still two huge challenges. To overcome these challenges, this paper designs an unbounded and puncturable ciphertext-policy ABE with arithmetic span program (<inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula>) scheme and presents an expressive data sharing and self-controlled fine-grained data deletion solution in cloud-assisted IoT, which allows data owners to efficiently encrypt and share data with various computable access policies, but also enables data owners and data users to independently delete specific data stored in the cloud. The designed <inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula> leverages unbounded ABE and puncturable encryption to support the flexible update of system parameters and the deletion of specific data. Also, the arithmetic span program access structure is combined to realize expressive data sharing. Moreover, the <inline-formula><tex-math notation="LaTeX">$\mathcal {UP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {CP}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ABE}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mathcal {ASP}$</tex-math></inline-formula> is adaptively secure in the standard model, and comprehensive performance evaluations demonstrate its practicability and scalability in cloud-assisted IoT.

Secure, Efficient, and Weighted Access Control for Cloud-Assisted Industrial IoT

In the cloud-assisted Industrial Internet of Things (IIoT), ciphertext-policy attribute-based encryption (CP-ABE) could help the data owner (DO) share his sensitive data via the cloud under self-defined access structures. Among general CP-ABE schemes, the decryption overhead, the key generation cost, and the ciphertext length increase with the number of involved attributes. Additionally, only regular attributes are taking into consideration rather than weighted attributes. In this article, we proposed a secure, efficient, and weighted access control scheme (SEWAC) for cloud-assisted IIoT applications. SEWAC enables the DO to formulate any fine-grained access structure over weighted attributes without making it more complicated. Furthermore, such weighted attributes would not add the length of ciphertext. SEWAC also supports online/offline key generation to alleviate the computational cost of the authority from answering mass key requests in the online phase, while most computational tasks are executed in the offline phase. The heavy decryption overhead is offloaded to the cloud. To ensure the cloud to honestly execute the process of outsourced decryption, we design an efficient batch verification method, which allows the user to spend only three bilinear pairing operations in checking the correctness of batch results. We also give the formal security proof of the proposed scheme. Comprehensive comparisons and implementation results indicate that SEWAC can better achieve weighted access control, compressed ciphertext length, efficient key generation, and the assurance of the outsourced decryption result.

A lightweight pairing-free ciphertext-policy attribute-based signcryption for cloud-assisted IoT

A lightweight pairing-free ciphertext-policy attribute-based signcryption for cloud-assisted IoT

An identity-based public auditing protocol in cloud-assisted IoT

An identity-based public auditing protocol in cloud-assisted IoT

Public Key Encryption with Authorized Equality Test on Outsourced Ciphertexts for Cloud-Assisted IoT in Dual Server Model

In cloud computing, the outsourced data face many privacy and security threats. To allow the cloud server to perform comparison, search, and classification on outsourced ciphertexts while simultaneously providing privacy guarantee, the encryption method that supports the ciphertext equality test is considered as a promising way. Users are able to authorize the cloud server to conduct the ciphertext equality test, so that two ciphertexts can be determined whether they encrypt the same message without being decrypted. In this process, users do not need to retrieve, decrypt, and then perform comparison on data; thus, the computing and communication efficiency can be greatly improved, and the privacy of user data can be guaranteed at the cloud server side. However, existing encryption schemes supporting authorized ciphertext equality test in the single server model cannot resist the keyword guessing attacks, and the solutions in the dual server model do not provide simultaneous authorization on two servers. To address these issues, this paper proposes a public key encryption scheme supporting authorized equality test on ciphertexts in the dual server model (PKE-AUT), where the primary server and secondary server must get the authorization from users before performing a sequential equality test on ciphertexts. Security and performance analysis demonstrate that the proposed PKE-AUT scheme not only guarantees the privacy of user data and authorization but also is practical in cloud-assisted IoT-related applications.

Anonymous decentralized attribute-based access control for cloud-assisted IoT

Attribute-Based Encryption (ABE) has emerged as powerful cryptographic tools to bring fine-grained access control with widespread applications such as Cloud-assisted IoT data sharing. Subsequently, decentralized ABE with untrusted attribute authorities is proposed to remove the online Trusted Authority (TA). In the decentralized architecture, a user as a data customer (e.g., IoT-device) submits his attributes to the untrusted authorities to get the private keys. In the architecture, user’s privacy, against the untrusted authorities, is a significant challenge that must be ensured (e.g., E-health Cloud application). In this paper, we address the privacy issue in the decentralized ABE and propose a novel anonymous and decentralized attribute-based encryption in the standard model. It preserves the user’s anonymity against the authorities in an efficient manner. In our solution, we use cryptographic accumulators to verify the user’s attributes anonymously. Then, we include the accumulator in the ciphertext to ensure the ABE access control against unauthorized users.Moreover, in some applications, access structures (encryption/decryption policy) include sensitive information and should be obfuscated from everyone minus the users whose secret key attributes meet the access structures. To ensure the hidden policy, we propose an efficient and decentralized policy obfuscation technique to preserve the privacy of the policy against the Public Cloud Server (PCS). It is exciting for a decentralized environment where the authorities are untrusted and may collude with the PCS.To be applicable for IoT resource-constrained devices, we outsource the expensive decryption computation over powerful Cloud servers. Then, we formally analyze the security properties of the proposed scheme and conduct experimental results to show its efficiency. Finally, we briefly explain how the features of the proposal meet the requirements of some real-world applications.

A Secure Key-Aggregate Keyword Retrieval Scheme Over Encrypted Data in Cloud Computing

Key-aggregate keyword retrieval primitive enables a cloud server on behalf of delegated users to securely perform a keyword search over the data encrypted with various public keys. However, existing key-aggregate searchable encryption schemes are insecure against keyword guessing attacks, which result in the privacy leakage of keyword ciphertext and trapdoor. In this paper, we for the first time formulate a secure and efficient key-aggregate searchable encryption for cloud-assisted IoT applications, which not only enables a data owner to securely share the selected documents to multiple users, but also supports data users in delegating the capability of keyword search to a cloud server for searching the desired documents without leaking any privacy of keyword ciphertext and trapdoor. Then, the security of the proposed scheme is formally defined and proven to be secure against the indistinguishable selective-file chosen keyword attacks. The flexibility and practicability of the formulated scheme is also demonstrated by theoretical evaluations and experimental simulations.

Cloud assisted big data information retrieval system for critical data supervision in disaster regions

Presently, the advancement of Cloud Assisted Big Data information retrieval system(CABDIRS) for heterogeneous data management plays a significant role in disaster management framework. In the recent past, facilitating disaster related activities such as Emergency information collection, sharing of exposed insights data about the region, and integration with local groups as well as global scale across various communities’ need assistance for precise and timely information retrieval framework concerning about disaster management. However, the available information retrieval system in the market has limited invariant integration model, whereas it provides improper sharing and collaboration capabilities in dynamic environment about the disaster areas. Hence, this research driving this exploration for powerful use of Cloud assisted big data system which uses Regression based information retrieval measurable computational model (RBIRMM) that offers to foresee the collection, sharing and integration of data in the disaster management regions. This paper features the integration of Cloud assisted IoT(CIoT)and Big data system for information retrieval system which assist the government in taking decisions during disaster conditions in an effective fasten manner. This paper feature the fundamental research that moves through experimental validation which has been conducted and reported with numerical data in a virtual environment. The RBIRMM achieves 98% of accuracy when compared to other traditional methods.

Fingerprint Recognition Strategies Based on a Fuzzy Commitment for Cloud-Assisted IoT: A Minutiae-Based Sector Coding Approach

In parallel with the rapid developments of cloud-assisted IoT, the corresponding security and privacy issue emerges as a challenge. Biometric recognition technologies are interesting and promising to reinforce traditional cryptographic and personal authentication systems for cloud-assisted IoT. However, the biometric information, if compromised, cannot be canceled and substituted easily. In this paper, based on fuzzy commitment protocol, a fingerprint recognition scheme using minutiae-based sector coding strategy is proposed for cloud-assisted IoT. In our approach, the minutiae of a fingerprint are classified into many designed sectors and then encoded according to their features. With the idea of fuzzy commitment, the key encryption process is accomplished by using BCH codes and Hash mappings. Our scheme, not only provides a flexible way to realize the fingerprint recognition between the complexity and security by designing error-correcting codes with different parameters, but also offers a good balance between the genuine accept rate (GAR) and the false acceptance rate (FAR) with customized sector coding strategies.