How does the nature of the data influence the design of Data privacy protection?

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During the research for this article, we spoke with IT professionals and conducted online research.

“Data privacy and its design depend on the nature of the data, and what country is involved. Data privacy laws vary from country to country. Each country has different laws that protect the privacy of its citizens, including laws that govern how data is collected and used. Mainly, Data security requires securing your database, application security, and finally data security, if you want to protect everything related to data.”

– Divya (Technical Architect)

Privacy of data is one of the most critical components of information security in today’s digital world. Individuals, organizations, and governments have become increasingly concerned about the risks associated with data breaches, identity theft, and other forms of cybercrime. This is caused by the growing amount of data collected and shared online.

Data Protection

There are a number of factors that can influence the design of data privacy protection measures.

These include the type of data, its sensitivity, and the risk associated with its exposure. Highly sensitive data, such as medical records, financial information, or identity documents, may require more stringent security measures, such as encryption and access controls.

As well as determining the types of privacy regulations and laws that need to be adhered to, the nature of the data can also determine the procedures for collecting, processing, and storing it.

Measures to protect data should be designed taking into consideration their nature, their intended use, and the potential risks they pose.

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How do you classify data privacy protection?

Data privacy protection can be classified into various categories, including:

  1. Personal Data: Data about an individual whose identity can be determined by their name, address, email address, telephone number, and date of birth.
  2. Sensitive Data: This type of information includes information about an individual’s race, ethnicity, religion, political beliefs, health status, sexual orientation, and criminal history.
  3. Personally Identifiable Information (PII): PII includes information such as social security numbers, driver’s license numbers, passport numbers, and bank account numbers that can identify an individual.
  4. Non-Personal Data: Non-personal data is information about users that cannot be used to identify them, such as website usage statistics, device information, and location information.
  5. Confidential Data: Confidential data includes information that is considered private and should only be accessed by authorized individuals, such as trade secrets, financial information, and medical records.
  6. Public Data: This group of information includes government records, public directories, and social media profiles.

In order to protect data and ensure its security, classifications help to identify the types of information that must be protected. Data protection levels differ depending on the type of data. Read how data security pushed the market of biometric products at

How Data Compliance plays an effective role to secure sensitive data, and uses?

  • Maintaining data compliance is essential for protecting sensitive data and preventing unauthorized access, use, or disclosure.
  • Data compliance refers to the set of policies, procedures, and regulations that organizations must follow to ensure the proper handling, processing, storage, and protection of data.
  • The types of data being processed, the location and industry of the organization determine which regulations and standards apply to data compliance. As an example, to ensure the privacy and security of patients’ health information, healthcare organizations must comply with the Health Insurance Portability and Accountability Act (HIPAA), while financial institutions must adhere to the Payment Card Industry Data Security Standard (PCI DSS).
  • Retention policies are often mandated by data compliance regulations. In order to reduce the risk of data exposure, these policies ensure that sensitive data isn’t kept for longer than necessary.
  • Organizations are often required to have a plan in place to respond to data breaches as part of regulatory compliance. As part of this plan, steps should be taken to contain the breach, notify individuals affected, and report it to regulators.
  • Most organizations handle their data through third-party vendors. In order to comply with data compliance regulations, organizations often make sure that their vendors adhere to the same regulations, and implement appropriate security measures to ensure that sensitive data is protected.
  • Compliance with data security regulations plays an important role in securing sensitive information. Data breaches can be prevented if organizations comply with appropriate legal and regulatory requirements.
  • There are significant fines, legal penalties, and reputational damage that can result from breaking data privacy regulations and industry standards. Privacy and security of sensitive data are therefore vital for organizations, and they must take proactive steps to ensure compliance with data regulations.
Privacy policy

A variety of activities are involved in data compliance

  1. Mapping and inventorying data: The process of identifying and cataloging all the information within an organization.
  2. Risk assessment: Identification of potential data security and privacy risks.
  3. Procedures and policies development: Setting up policies and procedures to protect and handle data.
  4. Employee training and awareness: Educating and training employees about data compliance policies.
  5. Compliance monitoring: Review regularly, and assess compliance with data privacy regulations and industry standards. In order to ensure that the company is not violating any laws or regulations, and that customer data is protected, compliance monitoring is necessary.

Protecting data depends on its subject, its specialty, and its significance. Data protection levels differ depending on the type of data. For example, sensitive personal information such as credit card numbers should be encrypted and stored securely.

However, less sensitive information such as web analytics data can be stored in a less secure system. Furthermore, high-value data should be given the highest level of protection, while low-value data can be stored with less stringent security measures.

Read more about Data privacy and Compliance at

How do work on data requirement models?

In order to create data requirement models, developers follow a structured process that involves several key steps.

Working on data requirement models typically involves the following steps:

*Determine what the business requirements are: In order to develop a data requirement model that supports business requirements, it’s necessary to begin by identifying those requirements. An understanding of the project’s objectives, the stakeholders’ needs, and the data required to support those goals is necessary for this to happen.

Perform an analysis of existing data: Next, they examine existing data sources to determine if they meet the project’s requirements and whether they are available. In order to identify data gaps that need to be collected or processed, data models, databases, and data flows can be examined.

Developing the data model: The developer can begin designing the data requirement model after analyzing the business requirements and existing data sources. It entails choosing the appropriate model type (for example, a relational model or a dimensional model) and creating the schema for the model.

Data refinement : A refinement process involves testing and refining the data model iteratively. The goal of this process is to identify any issues with the model and adjust the schema as needed to meet project requirements.

Data model implementation: Creating the necessary tables, views, and other database objects is the final step in implementing the data model. In order to create the necessary database objects, they need to write SQL or use a database management tool.

Finalizing the data model by testing and validating it: The data model must be tested and validated to ensure that it meets the project’s requirements. Data model accuracy is verified by testing it against real-world scenarios and producing appropriate results.

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