Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens' sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments' challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government's diverse roles in privacy preservation - regulator, enforcer, user, and service provider - and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

A smart weight scale, or smart scale, is an Internet of Things (IoT)-based scale that can measure metrics other than body weight using various sensors and send the information to the cloud. Meanwhile, the problem is that a person’s weight is considered personally identifiable information (PII) and needs to be preserved to comply with general data protection regulations (GDPR). Our research aim is to use K-Means for anonymization so that a privacy-preserving smart body scale becomes GDPR-compliant. The first step is to form a novel privacy-preserving smart body scale framework. We obtained the cardiovascular disease dataset containing personal weights from Kaggle. We apply random noise perturbation and k-means clustering for anonymization. We apply cardiovascular disease classification using gradient boosting. Finally, we compared the performance of the three anonymization methods with several metrics, including information loss, entropy, and distortion. Test results show that our elbow method shows that the optimum number of clusters for body weight is six. This number has passed the k-anonymity assessment. Furthermore, comparisons show that the k-means generalization performs better than noise perturbation with distortion, information loss, and entropy values 71.1, 0.001, and 15.6, respectively.