Attribute-based pseudonymity for privacy-preserving authentication in cloud services

Nowadays, the use of digital content or digital media is increasing day by day. Therefore, there is a need to protect the digital document from both unauthorized users and authorized users. The digital document should be protected from authorized users who try to redistribute it illegally. Digital watermarking techniques along with cryptography are insufficient to ensure an adequate level of security of digital media. The security of the transferring digital data in the modern world is also a big challenge because there is a high risk of security breaches. In this article, a secure technique of image fusion using hybrid domains (spatial and frequency) for privacy preserving and copyright protection is proposed. The proposed method provides a secure technique for the digital content in cloud environment. Two cloud services are used to develop this work, which eliminates the role of a trusted third party (TTP). First is the design of an infrastructure as a service (IaaS) to store different images with encryption processes to speed up the image fusion process and save storage space. Second, a Platform as a Service (PaaS) is used to enable the digital content to improve computation power and to increase the bandwidth. The prime objective of the proposed scheme is to transfer the digital media between a service provider and customer in a secure way using a hybrid domain along with cloud storage. Imperceptibility and robustness measures are used to calculate the performance of the proposed approach.

Download Full-text

Privacy Preserving Through Fireworks Algorithm Based Model for Image Perturbation in Big Data

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2015070103 ◽

2015 ◽

Vol 6 (3) ◽

pp. 41-58 ◽

Cited By ~ 15

Author(s):

Amine Rahmani ◽

Abdelmalek Amine ◽

Reda Mohamed Hamou ◽

Mohamed Elhadi Rahmani ◽

Hadj Ahmed Bouarara

Keyword(s):

Big Data ◽

Search Algorithm ◽

Iterated Local Search ◽

Privacy Preserving ◽

Cloud Services ◽

Local Search Algorithm ◽

Medical Studies ◽

Fireworks Algorithm ◽

The Third ◽

Highly Sensitive

Nowadays, Social networks and cloud services contain billions of users over the planet. Instagram, Facebook and other networks give the opportunity to share images. Users upload millions of pictures each day, including personal images. Another domain, which concerns medical studies, requires a highly sensitive medical images that retain personal details close to patients. Image perturbation have attracted a great deal of attention in the last few years. Many works concerning image ciphering and perturbing have been published. This paper deals with the problem of image perturbation for privacy preserving. The authors build three new systems that consist of hiding small details in pictures by rotating some pixels. Their models use two algorithms: the first one involves a simulation of the firework algorithm in which they place fireworks on selected pixels then represents sparks as rotation processes. The second system consists of a model of rotation based perturbation using iterated local search algorithm (ILS) with 2 optimization stages. Meanwhile, the third one consists of using the same principle of the previous system except by using the ILS algorithm with 3 optimization stages.

Download Full-text

Privacy Preservation of Healthcare Data in Hybrid Cloud using a Hybrid Meta-Heuristics Based Sanitization Technique

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4575.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 2882-2890

Keyword(s):

Side Effects ◽

Search Algorithm ◽

Cloud Model ◽

Gravitational Search Algorithm ◽

Data Access ◽

Privacy Preserving ◽

Cloud Services ◽

Sensitive Information ◽

Hybrid Cloud ◽

Healthcare Data

Over the recent years, the expansion of cloud computing services enable hospitals and institutions to transit their healthcare data to the cloud, thus it provides the worldwide data access and on-demand high quality services at a cheaper rate. Despite the benefits of healthcare cloud services, the associated privacy issues are widely concerned by individuals and governments. Privacy risks rise when outsourcing personal healthcare records to cloud due to the sensitive nature of health information and the social and legal implications for its disclosure. Over the recent years, a privacy-preserving data mining (PPDM) technique has become a critical issue for the problems. Our goal is to design a privacy-preserving outsourcing framework under the hybrid cloud model. In this work we propose a Hybrid Ant Colony Optimization and Gravitational Search Algorithm (ACOGSA) to express the problem of hiding sensitive data through transaction deletion. Thus, it reduces the side effects of the hybrid cloud. Substantive experiments will be carried to compare the performance of the designed algorithm with the state-of-the-art approaches in terms of the side effects and database similarity (integrity). Over the past to sanitize the databases used for hiding sensitive information, a few heuristic approaches have been proposed. The method used for the comparison involves GA, PSO, ACO, and Firefly framework.

Download Full-text

Refined cloud services discovery based on user model including privacy-preserving access control

Procedia Computer Science ◽

10.1016/j.procs.2018.08.015 ◽

2018 ◽

Vol 126 ◽

pp. 808-820

Author(s):

Sarra Abidi ◽

Mehrez Essafi ◽

Myriam Fakhri ◽

Henda Hajjami Ben Ghezala

Keyword(s):

Access Control ◽

Privacy Preserving ◽

User Model ◽

Cloud Services

Download Full-text

Efficient security solution for privacy-preserving cloud services

2013 36th International Conference on Telecommunications and Signal Processing (TSP) ◽

10.1109/tsp.2013.6613884 ◽

2013 ◽

Cited By ~ 7

Author(s):

Lukas Malina ◽

Jan Hajny

Keyword(s):

Privacy Preserving ◽

Cloud Services

Download Full-text

SecureDL: A privacy preserving deep learning model for image recognition over cloud

10.36227/techrxiv.13650059.v1 ◽

2021 ◽

Author(s):

Vishesh Kumar Tanwar ◽

Balasubramanian Raman ◽

Amitesh Singh Rajput ◽

Rama Bhargava

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Visual Information ◽

Low Cost ◽

Number System ◽

Privacy Preserving ◽

Cloud Services ◽

Binary Number ◽

Encryption Scheme ◽

Encrypted Data

<div>The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images’ visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes. </div>

Download Full-text

Privacy-preserving public auditing with data deduplication in cloud computing

10.32920/ryerson.14668344 ◽

2021 ◽

Author(s):

Naelah Abdulrahman Alkhojandi

Keyword(s):

Data Integrity ◽

Privacy Preserving ◽

Cloud Services ◽

Storage Efficiency ◽

Data Deduplication ◽

Public Auditing ◽

Efficient Data ◽

User Revocation ◽

Public Auditability ◽

And Storage

Storage represents one of the most commonly used cloud services. Data integrity and storage efficiency are two key requirements when storing users’ data. Public auditability, where users can employ a Third Part Auditor (TPA) to ensure data integrity, and efficient data deduplication which can be used to eliminate duplicate data and their corresponding authentication tags before sending the data to the cloud, offer possible solutions to address these requirements. In this thesis, we propose a privacy preserving public auditing scheme with data deduplication. We also present an extension of our proposed scheme that enables the TPA to perform multiple auditing tasks at the same time. Our analytical and experimental results show the efficiency of the batch auditing by reducing the number of pairing operations need for the auditing. Then, we extend our work to support user revocation where one of the users wants to leave the enterprise.

Download Full-text

Privacy-preserving public auditing with data deduplication in cloud computing

10.32920/ryerson.14668344.v1 ◽

2021 ◽

Author(s):

Naelah Abdulrahman Alkhojandi

Keyword(s):

Data Integrity ◽

Privacy Preserving ◽

Cloud Services ◽

Storage Efficiency ◽

Data Deduplication ◽

Public Auditing ◽

Efficient Data ◽

User Revocation ◽

Public Auditability ◽

And Storage

Storage represents one of the most commonly used cloud services. Data integrity and storage efficiency are two key requirements when storing users’ data. Public auditability, where users can employ a Third Part Auditor (TPA) to ensure data integrity, and efficient data deduplication which can be used to eliminate duplicate data and their corresponding authentication tags before sending the data to the cloud, offer possible solutions to address these requirements. In this thesis, we propose a privacy preserving public auditing scheme with data deduplication. We also present an extension of our proposed scheme that enables the TPA to perform multiple auditing tasks at the same time. Our analytical and experimental results show the efficiency of the batch auditing by reducing the number of pairing operations need for the auditing. Then, we extend our work to support user revocation where one of the users wants to leave the enterprise.

Download Full-text

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

Security and Communication Networks ◽

10.1155/2018/5841967 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Hongliang Zhu ◽

Meiqi Chen ◽

Maohua Sun ◽

Xin Liao ◽

Lei Hu

Keyword(s):

Cloud Computing ◽

Homomorphic Encryption ◽

Low Cost ◽

Bloom Filter ◽

Privacy Preserving ◽

Cloud Services ◽

Multimedia Application ◽

Computation Method ◽

Multimedia Processing ◽

Set Intersection

With the development of cloud computing, the advantages of low cost and high computation ability meet the demands of complicated computation of multimedia processing. Outsourcing computation of cloud could enable users with limited computing resources to store and process distributed multimedia application data without installing multimedia application software in local computer terminals, but the main problem is how to protect the security of user data in untrusted public cloud services. In recent years, the privacy-preserving outsourcing computation is one of the most common methods to solve the security problems of cloud computing. However, the existing computation cannot meet the needs for the large number of nodes and the dynamic topologies. In this paper, we introduce a novel privacy-preserving outsourcing computation method which combines GM homomorphic encryption scheme and Bloom filter together to solve this problem and propose a new privacy-preserving outsourcing set intersection computation protocol. Results show that the new protocol resolves the privacy-preserving outsourcing set intersection computation problem without increasing the complexity and the false positive probability. Besides, the number of participants, the size of input secret sets, and the online time of participants are not limited.

Download Full-text