Towards an Architecture-based Ensemble Methods for Online Social Network Sensitive Data Privacy Protection

Abstract

<p><strong>Abstract - </strong>In 2014, the world woke up to a giant data breach that leveraged users’ personal information that was taken from one of the world’s biggest social network platform. Based on the literature, this was possible because of the Centralised Architectural-based Approach to protecting the privacy of users’ online data. Although the literature is inundated with decentralized approaches, there is none to the best of our knowledge that uses an ensemble of methods and draws on a consensus mechanism to address the challenges caused by the Centralised Architectural-based Approach. This paper presents a decentralized approach that adopts and adapts an ensemble of methods. These methods include cryptographic, hashing, and the plenum byzantine fault tolerance algorithms that present a consensus platform, protocol, and mechanism to use the technology of blockchain in a novel manner as a significant contribution. This paper adopts the descriptive approach in its presentation as the usable implementation of the presented proposal is near completion with issues of computational overhead addressed based on preliminary results that show promise of being able to support agreement up to ≈ 75% in terms of making changes by participants in the chain. <strong></strong></p>

Adaptive Sensitive Information Recognition Based on Multimodal Information Inference in Social Networks

“…In addition, data encryption for privacy protection is a common means. Idepefo et al [9] combined blockchain technology with cryptography, hash, and consensus mechanisms. Xie et al [10] proposed a hybrid data method based on homomorphic encryption and AES and constructed a multiclass support vector machinebased privacy-preserving medical data sharing system.…”

Noise Management in Homomorphic Encryption Scheme: Analyzing of Algorithm

“…The following lines describe the types of increasing noise in ciphertexts during homomorphic operations and present the most commonly used noise management strategies. Homomorphic encryption scheme When the encrypted data is stored on cloud [20,21], all kinds of computations [19] such as analyzing data, the calculation of variance, data mining, … are performed after prior decryption. The prior decryption brings an additional cost in terms of resources and time and introduces a possibility of loss of confidentiality and privacy.…”