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New quality that has been delivered by the provisions of General Data Protection Regulation (GDPR) (EU) 2016/679 is intended to secure a higher level of safety for personal data processing operations. The following elaboration was produced as an attempt to address the questions regarding practical methods of implementation for the indispensable mechanism of GDPR compliance. The guidelines contained in the article are supposed to be helpful in enhancing the safety level for processed personal data. Theoretical and legal studies over the status and functioning of the valid legislation with reference to the practical application of personal data processing procedures have been applied in the article. The main sources of knowledge included valid legal acts, opinions from Article 29 Working Party, technical norms as well as available general knowledge. The outcomes of the said studies indicated the complexity of the issue and established the necessity to continue further studies in practical implementation methods, such as the national and European mechanism of certification or sector codes of good practices.

Edge storage is driven by the emerging edge computing and application intelligence, which makes the data anonymization become essential to guarantee the security of the data. The risk with data anonymization is that it can be re-identified, where anonoymized data is matched with available information to discover the individual. Appropriate data anonymization techniques can help on reducing the risk of re-identification. It is very challenging to protect data privacy against re-identification attacks without significantly reduce usefulness of the data. This work proposes an evaluation framework for data anonymization techniques to measure the risks of re-identification and usefulness of anonymized data.

Machine learning algorithms, such as KNN, SVM, MLP, RF, and MLR, are used to extract valuable information from shared digital data on social media platforms through their APIs in an effort to identify anonymous publishers or online users. This can leave these anonymous publishers vulnerable to privacy-related attacks, as identifying information can be revealed. Twitter is an example of such a platform where identifying anonymous users/publishers is made possible by using machine learning techniques. To provide these anonymous users with stronger protection, we have examined the effectiveness of these techniques when critical fields in the metadata are masked or encrypted using tweets (text and images) from Twitter. Our results show that SVM achieved the highest accuracy rate of 95.81% without using data masking or encryption, while SVM achieved the highest identity recognition rate of 50.24% when using data masking and AES encryption algorithm. This indicates that data masking and encryption of metadata of tweets (text and images) can provide promising protection for the anonymity of users’ identities.

This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information.It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors' comments.