Implementation of Differential Privacy for Enhancing Data Privacy in Wearable Fitness Trackers

The availability of fitness wearable as devices has gradually been on the rise has made the monitoring of individual health, and frequent routines a thing a further degree, but at the same time making privacy questions at the same time. The leakage of information related to wearable fitness tracking devices are rather frequent, it puts personal information at risk of being misused, identity loss and theft, and money loss. This research addresses the important question of how to protect users' and audience's personal data of wearable fitness technology by examining a secure and privacy-conscious methods, with a special reference to Differential Privacy (DP). DP provides providing a mathematical context which guarantees data integrity information while revealing new ideas that can be used without violating the privacy of the participants personal information. This study also explores the usage of DP mechanisms, namely Laplacian and Gaussian mechanisms developed for analysing data collected from wearable fitness tracking devices to maintain privacy at the same time serving the purpose of utility of data. Key questions examined knowledge areas include; Inferences from fitness tracker information and the consequences of utilisation of DP mechanisms. The analysis demonstrates that Laplace mechanism for sleep data is effective slight oscillations from the initial values, thus enabling the preservation of their traditional meanings. The proposed filters enhance the quality of the dataset and its statistically related attributes as well. Similarly, thus, the Gaussian mechanism contributes to data accuracy with minimal variations. Through further analysis, this work shows that Differential Privacy mechanisms can accurately balance privacy and use of data for users and manufacturers.