# Privacy-Preserving Data Analytics in Internet of Medical Things
###### Abstract  
The healthcare sector has changed dramatically in recent years due to depending more and more on big data to improve patient care, enhance or improve operational effectiveness, and forward medical research. Protecting patient privacy in the era of digital health records is a major challenge, as there could be a chance of privacy leakage during the process of collecting patient data. To overcome this issue, we propose a secure, privacy-preserving scheme for healthcare data to ensure maximum privacy of an individual while also maintaining their utility and allowing for the performance of queries based on sensitive attributes under differential privacy. We implemented differential privacy on two publicly available healthcare datasets, the Breast Cancer Prediction Dataset and the Nursing Home COVID-19 Dataset. Moreover, we examined the impact of varying privacy parameter (\(\varepsilon\)) values on both the privacy and utility of the data. A significant part of this study involved the selection of \(\varepsilon\), which determines the degree of privacy protection. We also conducted a computational time comparison by performing multiple complex queries on these datasets to analyse the computational overhead introduced by differential privacy. The outcomes demonstrate that, despite a slight increase in query processing time, it remains within reasonable bounds, ensuring the practicality of differential privacy for real-time applications. differential privacy; healthcare data; data sharing; user privacy; data utility 2020  
Differential privacy has emerged as a strong and provable privacy guarantee model to address this paradox by safeguarding data privacy and preserving the analytical usefulness of datasets [3]. Differential privacy preserves individual personal identifiable information by proving mathematically that the output distribution of a query remains independent of whether an individual record is present or not in the datasets. By adding calibrated noise to the query output or data, a balance between data privacy and data accuracy can be established without compromising the overall insights that can be derived from the information. The application of differential privacy in the healthcare sector is particularly appealing, as the sector requires accurate and comprehensive data for research and industrial purposes. In healthcare organizations, a trusting environment can be built among researchers, patients, doctors, and other participants by implementing differential privacy to ensures the protection of data shared publicly or with any third party for research purposes, policy making, and collaborative initiatives. Despite its potential, the implementation of differential privacy also faces challenges in the healthcare industry [4]. These include the potential impact on the accuracy of clinical and research findings, the challenges of precisely calibrating noise to preserve data utility, and the need for robust legal and ethical frameworks to oversee its deployment [5]. To solve these concerns, a multidisciplinary approach is required that considers moral dilemmas, robust policy development, and other advancements. In contrast to other differential privacy implementations, we made an attempt to maintain patient data privacy in the field of electronic healthcare data by answering the complicated, complex queries under differential privacy. Additionally, we evaluate our approach against other differential privacy frameworks by showing how well it performs across a range of privacy parameter values, highlighting its robustness and adaptability in preserving data utility while guaranteeing strong privacy protections. We perform a detailed time complexity analysis, showing how effective our approach is at handling complex queries in reasonable amounts of computational time. Next, we implement our method on datasets of different sizes to evaluate its scalability. Our results indicate that our approach performs consistently at various scales, effectively handling both small and large datasets.