# Data Privacy and Ethical Considerations in Database Management
## 2 Literature Review  
This section presents works related to data privacy and ethical concerns in database management. In one paper [12], the authors present a tool used in PostgreSQL to mask and anonymize the information of a database. They show techniques like masking parts of the text commonly applied to credit card numbers, where only the last four digits remain visible; date aging that substitutes the value with a range of ages; and nullifying columns where the actual values are swapped for a null value. This technique shows an example of how to help database managers protect critical user information to prevent data infringements. Another paper [13] presents an analysis of the performance of encryption tools over three databases, where one of the databases mentioned is PostgreSQL. In this database, the authors applied a tool called pgcrypto. This tool provides several functions, such as creating password hashes, Pretty Good Privacy (PGP) encryption functions, and random hash functions. The authors applied these security measures and evaluated the performance of databases given different workloads. The author states that PostgreSQL provides good security over data protection, although its performance may be slightly impacted. The authors proposed a study on privacy methods and anonymization techniques to protect sensitive data on databases to prevent attacks and data recognition to evaluate its performance [14]. Some methods applied were k-anonymity, l-diversity, suppression, generalization, anatomization, and slicing. The authors deduce that they must lose some information to protect data privacy and prevent data recognition, even though attacks can still be performed. The authors of [15] present a study that analyses an anonymization process of databases of banking applications while applying anonymization methodologies to improve its security. The authors found some challenges, given that the anonymization process can be very complex and time consuming, mainly when dealing with vast amounts of data. In addition, they explain the different contexts, whether organizational, functional/business, or technical, when applying anonymization techniques. Article [16] addresses the possible risks and challenges related to data minimization, which consists of limiting the amount of personal information collected, stored, and processed to only what is necessary to achieve a specific objective. Despite being commonly recommended as an excellent privacy measure, the article points out that data minimization can have unexpected and potentially harmful consequences. In one paper, the researchers demonstrated that, by carefully minimizing the data collected about a student's daily activities, they could still obtain valuable insights for personalized learning while effectively protecting the student's privacy. This application shows how data minimization can be used creatively in specific contexts to combine the need for data-driven decision making with robust privacy protections [17]. Other authors published a study outlining a methodology for pseudonymizing medical data [18]. This involved hashing and encrypting patient identifiers and securely storing the encryption key, allowing for data analysis while preserving patient confidentiality. A study carried out in [19] highlights the importance of choosing algorithms when comparing data encryption performance. The research concluded that, while solid encryption is essential, the algorithm also needs to ensure adequate processing to avoid possible negative impacts on the performance of the selected database. In the context of ethical considerations in data management, the Royal Society's report on \"Privacy Enhancing Technologies\" [20] provides valuable insights into the intersection of technology and privacy. The report discusses various technologies and methodologies that can be employed to enhance privacy in data-intensive applications, which is particularly relevant to our discussion on ethical database management.