Huff-DP: Huffman Coding based Differential Privacy Mechanism for Real-Time Data
Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

TL;DR
This paper introduces Huff-DP, a novel differential privacy mechanism leveraging Huffman coding and adaptive privacy budget selection, tailored for real-time data with underlying patterns, ensuring privacy while optimizing computational efficiency.
Contribution
It proposes a new Huff-DP mechanism that adaptively determines privacy budgets based on data patterns using static, sine, and fuzzy logic algorithms.
Findings
Effective privacy protection demonstrated in experiments.
Reduces computational cost of privacy budget determination.
Maintains data utility while safeguarding privacy.
Abstract
With the advancements in connected devices, a huge amount of real-time data is being generated. Efficient storage, transmission, and analysation of this real-time big data is important, as it serves a number of purposes ranging from decision making to fault prediction, etc. Alongside this, real-time big data has rigorous utility and privacy requirements, therefore, it is also significantly important to choose the handling strategies meticulously. One of the optimal way to store and transmit data in the form of lossless compression is Huffman coding, which compresses the data into a variable length binary stream. Similarly, in order to protect the privacy of such big data, differential privacy is being used nowadays, which perturbs the data on the basis of privacy budget and sensitivity. Nevertheless, traditional differential privacy mechanisms provide privacy guarantees. However, on the…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Vehicular Ad Hoc Networks (VANETs) · Wireless Communication Security Techniques
