Abstract: Data collection using local differential privacy (LDP) has mainly been studied for homogeneous data. Several data categories, including key-value pairs, must be estimated simultaneously in real-world applications, including the frequency of keys and the mean values within each key. It is challenging to achieve an acceptable utility-privacy tradeoff using LDP for key-value data collection since the data has two aspects, and a client could have multiple key-value pairs. Current LDP approaches are not scalable enough to handle large and small datasets. When the dataset is small, there is insufficient data to calculate statistical parameters; when the dataset is enormous, such as in streaming data, there is a risk of data leakage due to the high availability of too much information. The result is unsuitable for examination due to the substantial amount of randomization used in some methods. Existing LDP approaches are mostly restricted to basic data categories like category and numerical values. To address these difficulties, this research developed the DKVALP (Differentially private key-value pairs) algorithm, which ensures differential privacy in key-value pair data. This DKVALP is a lightweight, differentially private data algorithm that generates random noise using an updated Laplace algorithm to ensure differential privacy for the data. According to execution outputs on synthetic and real-world datasets, the proposed DKVALP framework offers improved usefulness for both frequency and mean predictions over the similar LDP security as conventional approaches.
Keywords: Differential privacy, local differential privacy, Laplace algorithm, back key-value pairs, improved Laplace algorithm and DKVALP
