Towards a distributed ABE based approach to protect privacy on online social networks

Yahiatene, Youcef; Menacer, Djamel Eddine; Riahla, Mohamed Amine; Rachedi, Abderrezak; Tebibel, Thouraya Bouabana

doi:10.1109/wcnc.2019.8886021

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Two improved algorithms for rule pruning and policy refinement were also proposed to generate higher-quality mining strategies. Yahiatene et al [16] proposed a framework called CloudSN to mitigate privacy issues in OSNs. Using a distributed multi-authority ABE scheme, it provides flexible access to private data, which can only be accessed by users with the correct keys.…”

Section: Attribute Based Access Control Modelmentioning

confidence: 99%

Deep Learning Social Network Access Control Model Based on User Preferences

Shan,

Li,

Wang

et al. 2024

CMES

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A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes to expand the depth and breadth of user preferences. Finally, a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes: allow access and deny access. The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network. The model is validated using a dataset and compared with other methods without losing generality. The model improved accuracy by 2.18% compared to the baseline method GraphSAGE, and improved F1 score by 1.45% compared to the baseline method, verifying the effectiveness of the model.

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Section: Attribute Based Access Control Modelmentioning

confidence: 99%

Deep Learning Social Network Access Control Model Based on User Preferences

Shan,

Li,

Wang

et al. 2024

CMES

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show abstract

“…It is demonstrated that the scheme is secure in the random oracle model and has good practicality. Yahiatene et al [30] proposed a new framework to control access to the private data on online social networks. The framework is based on cloud storage and exploits a distributed multi-authority ABE model.…”

Section: Related Workmentioning

confidence: 99%

SEM-ACSIT: Secure and Efficient Multiauthority Access Control for IoT Cloud Storage

Xiong

Wang

et al. 2020

IEEE Internet Things J.

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Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively applicable to IoT applications. In this paper, we construct a new CP-ABE based storage model for data storing and secure access in a cloud for IoT applications. Our new framework introduces an attribute authority management (AAM) module in the cloud storage system functioned as an agent that provides a userfriendly access control and highly reduces the storage overhead of public keys. Then, we propose a novel secure and efficient multiauthority access control scheme of the cloud storage system for IoT, namely SEM-ACSIT, which obtains both backward security and forward security when an attribute of a user is revoked. By exploiting encryption outsourcing, simplified key structuring and the AAM module, computational overhead of a user is immensely decreased. Moreover, a user access control list (UACL) in the cloud server is constructed newly to support authorization access for a specific user. The analysis and simulation results demonstrate that our SEM-ACSIT scheme achieves powerful security with less computational overhead and lower storage cost than existing schemes.

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“…This challenge is specifically severe for medical and financial data because users have the right to decide who can review their sensitive medical and financial records. With attribute-based encryption (ABE) [4][5][6][7][8][9], we can make fine-grained access control much more manageable by only allowing some people with specified attributes (i.e., conditions) to access and view the files.…”

Section: Introduction 1motivationsmentioning

confidence: 99%

Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes

Lin

2023

Cryptography

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Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms. However, most existing schemes only support single-keyword or provide no ranking searching results, which could be inflexible and inefficient in satisfying the real world’s actual needs. We propose a flexible multi-keyword ranked searchable attribute-based scheme using search trees to overcome the above-mentioned problems, allowing users to combine their fuzzy searching keywords with AND–OR logic gates. Moreover, our enhanced scheme not only improves its privacy protection but also goes a step further to apply a semantic search to boost the flexibility and the searching experience of users. With the proposed index-table method and the tree-based searching algorithm, we proved the efficiency and security of our schemes through a series of analyses and experiments.

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Towards a distributed ABE based approach to protect privacy on online social networks

Cited by 10 publications

References 16 publications

Deep Learning Social Network Access Control Model Based on User Preferences

Deep Learning Social Network Access Control Model Based on User Preferences

SEM-ACSIT: Secure and Efficient Multiauthority Access Control for IoT Cloud Storage

Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes

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