# Privacy-Preserving Data Analytics in Internet of Medical Things
## 6 Conclusions and Future Work  
This study proposed a differential privacy-based method for protecting healthcare data on the Internet of Medical Things. Initially, this study examined conventional approaches that were employed in the electronic healthcare data privacy process prior to the application of differential privacy. Then, we performed an in-depth analysis of differential privacy and its core characteristics. The practical implementation showcased promising experimental results, demonstrating the application of differential privacy mechanisms across multiple queries. Variations in the privacy parameter, i.e. the privacy budget, were analysed to illustrate their impact on the preservation of privacy while maintaining data utility. Comparative analyses involving Laplace and Gaussian mechanisms were conducted by analysing both schemes in terms of their ability to meet privacy and security requirements with minimal computational overhead. Furthermore, we carried out a thorough examination of time complexity through the application of differential privacy to complex queries on datasets of various sizes.  
Even the DP mechanism is sufficiently effective in providing the necessary data privacy protection, but there exist still some limitations that need to be addressed to maintain privacy. In adversarial scenarios, attackers can attempt to infer sensitive information by analysing the outputs of multiple queries. Even with differential privacy, if they have prior knowledge or can infer relationships within the data, they may extract sensitive insights. To prevent such query inference attacks, robust logging and auditing mechanisms can be implemented for queries. This can help identify suspicious patterns and flag potentially adversarial queries. Restrictions can be put on how many queries a user can send in a certain amount of time to reduce the possibility of inference attacks caused by excessive querying. When complex queries are executed under differential privacy, SQL injection attacks can compromise the privacy mechanism. Attackers can modify the behaviour of queries by injecting SQL code to access raw data rather than the differentially private output, effectively bypassing privacy protections. Strict input validation and sanitization should be used to mitigate this by ensuring that only valid data types and formats are accepted. However, differential privacy is not always adaptable enough to use in every real-world situation, which could make it more difficult to achieve the required levels of security and usability. As a result, it would be ideal to examine and customize other mechanisms in the future. Applying data-dependent differential privacy to real-world datasets, where databases comprising tuple correlations that signify relations betweendifferent tables in the database, may expose limitations in the underlying assumptions of this privacy model. In such cases, inference attacks may exist under the differential privacy mechanism. Thus, future research should take this into account to create a better mechanism that enhances the current approach.  
conceptualization, B.M., F.K. and S.T.; methodology, B.M., F.K., S.I.S. and Q.H.A.; investigation, B.M., F.K., S.I.S. and Q.H.A.; supervision, F.K., S.T. and S.A.S.; funding acquisition, S.A.S.; writing--original draft, preparation, B.M.; writing--review and editing, B.M., F.K., S.T., S.A.S., S.I.S. and Q.H.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by the Engineering and Physical Sciences Research Council (EPSRC) (grant # EP/W037076/1).  
Data Availability Statement: Data are available upon request to the corresponding author.  
The authors declare no conflicts of interest.