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 other hand, real-time data cannot be dealt as an ordinary set of records, because it usually has certain underlying patterns and cycles, which can be used for forming a link to a specific individuals private information that can lead to severe privacy leakages (e.g., analysing smart metering data can lead to classification of individuals daily routine). Thus, it is equally important to develop a privacy preservation model, which preserves the privacy on the basis of occurrences and patterns in the data. In this paper, we design a novel Huff-DP mechanism, which selects the optimal privacy budget on the basis of privacy requirement for that specific record. In order to further enhance the budget determination, we propose static, sine, and fuzzy logic based decision algorithms. From the experimental evaluations, it can be concluded that our proposed Huff-DP mechanism provides effective privacy protection alongside reducing the privacy budget computational cost.