  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.
