scholarly journals Survey on Revocation in Ciphertext-Policy Attribute-Based Encryption

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1695 ◽  
Author(s):  
Al-Dahhan ◽  
Shi ◽  
Lee ◽  
Kifayat
Keyword(s):  
Computational Cost ◽  
Sensitive Data ◽  
Resource Limited ◽  
Attribute Based Encryption ◽  
Wide Range ◽  
Secure Cloud Storage ◽  
Iot Devices ◽  
Ciphertext Policy ◽  
Cryptographic Techniques ◽  
High Computational Cost

Recently, using advanced cryptographic techniques to process, store, and share datasecurely in an untrusted cloud environment has drawn widespread attention from academicresearchers. In particular, Ciphertext‐Policy Attribute‐Based Encryption (CP‐ABE) is a promising,advanced type of encryption technique that resolves an open challenge to regulate fine‐grainedaccess control of sensitive data according to attributes, particularly for Internet of Things (IoT)applications. However, although this technique provides several critical functions such as dataconfidentiality and expressiveness, it faces some hurdles including revocation issues and lack ofmanaging a wide range of attributes. These two issues have been highlighted by many existingstudies due to their complexity which is hard to address without high computational cost affectingthe resource‐limited IoT devices. In this paper, unlike other survey papers, existing single andmultiauthority CP‐ABE schemes are reviewed with the main focus on their ability to address therevocation issues, the techniques used to manage the revocation, and comparisons among themaccording to a number of secure cloud storage criteria. Therefore, this is the first review paperanalysing the major issues of CP‐ABE in the IoT paradigm and explaining the existing approachesto addressing these issues.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

scholarly journals Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fei Meng ◽  
Leixiao Cheng ◽  
Mingqiang Wang
Keyword(s):  
Smart City ◽  
Keyword Search ◽  
Sensitive Information ◽  
Security Model ◽  
Computational Overhead ◽  
Diffie Hellman ◽  
Attribute Based Encryption ◽  
Iot Devices ◽  
Ciphertext Policy ◽  
Access Policies

AbstractCountless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

scholarly journals CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs

2020 ◽  
Author(s):  
Li'an Zhuo ◽  
Baochang Zhang ◽  
Hanlin Chen ◽  
Linlin Yang ◽  
Chen Chen ◽  
...  
Keyword(s):  
Computational Cost ◽  
Unified Framework ◽  
Embedded Devices ◽  
Natural Approach ◽  
Neural Architecture ◽  
Resource Limited ◽  
Training Stage ◽  
Comparable Accuracy ◽  
Kernel Level ◽  
High Computational Cost

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architectures, which are still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework. To this end, a Child-Parent model is introduced to a differentiable NAS to search the binarized architecture(Child) under the supervision of a full-precision model (Parent). In the search stage, the Child-Parent model uses an indicator generated by the parent and child model accuracy to evaluate the performance and abandon operations with less potential. In the training stage, a kernel level CP loss is introduced to optimize the binarized network. Extensive experiments demonstrate that the proposed CP-NAS achieves a comparable accuracy with traditional NAS on both the CIFAR and ImageNet databases. It achieves an accuracy of 95.27% on CIFAR-10, 64.3% on ImageNet with binarized weights and activations, and a 30% faster search than prior arts.

scholarly journals A Lightweight Fine-Grained Search Scheme over Encrypted Data in Cloud-Assisted Wireless Body Area Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingsheng Cao ◽  
Luhan Wang ◽  
Zhiguang Qin ◽  
Chunwei Lou
Keyword(s):  
Keyword Search ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Security And Privacy ◽  
Body Area ◽  
Encrypted Data ◽  
Fine Grained ◽  
Resource Limited ◽  
Attribute Based Encryption ◽  
Ciphertext Policy

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.

CONCEPTUAL FRAMEWORK FOR LIGHTWEIGHT CIPHERTEXT POLICY-ATTRIBUTE BASED ENCRYPTION SCHEME FOR INTERNET OF THINGS DEVICES

2019 ◽  
Vol 4 (1) ◽  
pp. 237
Author(s):  
Nurhidayah Muhammad ◽  
Jasni Mohamad Zain
Keyword(s):  
Data Security ◽  
Group Communication ◽  
The Internet ◽  
Lightweight Cryptography ◽  
Small Group Communication ◽  
Attribute Based Encryption ◽  
Hash Algorithm ◽  
Iot Devices ◽  
Ciphertext Policy ◽  
Internet Of Thing

The purpose of this paper is to propose a conceptual model for data security in the Internet of thing devices. Estimated by Jumoki in early 2018 to 2022, there will be about 18 billion connected IoT devices. Therefore many issue related to IoT devices were discussed especially data security. Cryptography with lightweight features is one of the focus area by researchers to develop a powerful cryptography scheme for IoT devices. Lightweight cryptography scheme has been discussed and proposed widely recently. There are AES, PRESENT, Hash algorithm declared as a lightweight algorithm under consideration in ISO/IEC 29192 “Lightweight Cryptography”. Unfortunately these lightweight algorithm is one-to-one communication cryptography technique. This algorithm is very practical to implement for individuals or for small group communication but unpractical when implemented in a big company where many users can become a bottleneck. Therefore we propose a lightweight Ciphertext Policy-Attribute Based Encryption (CP-ABE) algorithm to implement in IoT devices. CP-ABE algorithm is one-to-many technique suitable for secure grouping communication, but this algorithm is not a lightweight feature. Therefore this paper proposes a lightweight CP-ABE algorithm for IoT devices.  

scholarly journals Medical Health Record Protection Using Ciphertext-Policy Attribute-Based Encryption and Elliptic Curve Digital Signature Algorithm

2019 ◽  
Vol 7 (1) ◽  
pp. 151-175
Author(s):  
Novi Aryani Fitri ◽  
M. Udin Harun Al Rasyid ◽  
Amang Sudarsono
Keyword(s):  
Elliptic Curve ◽  
Digital Signature ◽  
Data Center ◽  
Data Encryption ◽  
Sensitive Data ◽  
Efficient System ◽  
Storage Mechanism ◽  
Digital Signature Algorithm ◽  
Attribute Based Encryption ◽  
Ciphertext Policy

Information on medical record is very sensitive data due to the number of confidential information about a patient's condition. Therefore, a secure and reliable storage mechanism is needed so that the data remains original without any changes during it was stored in the data center. The user must go through an authentication process to ensure that not an attacker and verify to ensure the authenticity and accuracy of the data received. In this research, we proposed a solution to secure medical data using the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Elliptic Curve Digital Signature Algorithm (ECDSA) methods. Our system can secure data centers from illegal access because the uploaded data has patient control over access rights based on attributes that have been embedded during the data encryption process. Encrypted data was added to the digital signature to pass the authentication process before being sent to the data center. The results of our experiments serve efficient system security and secure with low overhead. We compare the proposed system performance with the same CP-ABE method but don’t add user revocation to this system and for our computing times are shorter than the previous time for 0.06 seconds and 0.1 seconds to verify the signature. The total time in the system that we propose requires 0.6 seconds.

Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification

2019 ◽  
Vol 62 (8) ◽  
pp. 1166-1177 ◽  
Author(s):  
Yuzhao Cui ◽  
Qiong Huang ◽  
Jianye Huang ◽  
Hongbo Li ◽  
Guomin Yang
Keyword(s):  
Sensitive Data ◽  
Encrypted Data ◽  
Attribute Based Encryption ◽  
Secret Keys ◽  
Share Data ◽  
Efficient Data ◽  
Ease Of Access ◽  
Ciphertext Policy ◽  
Cloud Servers ◽  
Equality Test

Abstract Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security.

scholarly journals Traceable Ciphertext-Policy Attribute-Based Encryption with Verifiable Outsourced Decryption in eHealth Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qi Li ◽  
Hongbo Zhu ◽  
Zuobin Ying ◽  
Tao Zhang
Keyword(s):  
Personal Health Information ◽  
Personal Health ◽  
Private Key ◽  
Resource Limited ◽  
Security Proof ◽  
Attribute Based Encryption ◽  
Identity Privacy ◽  
Outsourced Decryption ◽  
The One ◽  
Ciphertext Policy

In cloud-assisted electronic health care (eHealth) systems, a patient can enforce access control on his/her personal health information (PHI) in a cryptographic way by employing ciphertext-policy attribute-based encryption (CP-ABE) mechanism. There are two features worthy of consideration in real eHealth applications. On the one hand, although the outsourced decryption technique can significantly reduce the decryption cost of a physician, the correctness of the returned result should be guaranteed. On the other hand, the malicious physician who leaks the private key intentionally should be caught. Existing systems mostly aim to provide only one of the above properties. In this work, we present a verifiable and traceable CP-ABE scheme (VTCP-ABE) in eHealth cloud, which simultaneously supports the properties of verifiable outsourced decryption and white-box traceability without compromising the physician’s identity privacy. An authorized physician can obtain an ElGamal-type partial decrypted ciphertext (PDC) element of original ciphertext from the eHealth cloud decryption server (CDS) and then verify the correctness of returned PDC. Moreover, the illegal behaviour of malicious physician can be precisely (white-box) traced. We further exploit a delegation method to help the resource-limited physician authorize someone else to interact with the CDS. The formal security proof and extensive simulations illustrate that our VTCP-ABE scheme is secure, efficient, and practical.

scholarly journals An enhanced media ciphertext-policy attribute-based encryption algorithm on media cloud

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090819
Author(s):  
Hao Li ◽  
Lianbing Deng ◽  
Cheng Yang ◽  
Jianbo Liu
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Multiple Access ◽  
Control Structure ◽  
Computational Cost ◽  
Encryption Algorithm ◽  
Practical Significance ◽  
Attribute Based Encryption ◽  
Ciphertext Policy ◽  
Media Cloud

With the development of cloud computing, more and more video services are moving to the cloud. How to realize fine-grained access control of those data on cloud becomes an urgent problem. Attribute-based encryption provides a solution. However, heavy computation is still a bottleneck restricting the wider application of attribute-based encryption in cloud computing. In addition, we find that expression of the access control structure on media cloud can be further improved. To solve these problems, we propose an enhanced media ciphertext-policy attribute-based encryption algorithm and introduce its two key components, the multiple access tree and the key chain. To increase the scalability of the proposed algorithm, we discussed the issues of multi-authorization, user revocation, and 1 − n multiple access tree. Security analysis shows that enhanced media ciphertext-policy attribute-based encryption can successfully resist chosen-plaintext attacks under the decisional bilinear Diffie–Hellman assumption. Performance analysis proves both theoretically and practically that the proposed algorithm incurs less computational cost than the traditional ciphertext-policy attribute-based encryption, multi-message ciphertext-policy attribute-based encryption, and scalable ciphertext-policy attribute-based encryption by optimizing the access control structure. Our proposed algorithm has strong practical significance in media cloud.

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