Data Privacy & Protection

Data privacy & protection is a complex issue that affects the way organizations handle customer and employee personal information. Without adequate standards and protocols in place, companies risk legal action and credibility issues.

Data privacy focuses on how an individual’s personal data is collected, used and shared. Security focuses on protecting the data from malicious attacks and prevents the exploitation of stolen data for profit.

Privacy by design

Privacy by design is a term that refers to the practice of building privacy protections into systems, processes and policies from the beginning of the development lifecycle. This can include all stages of a project, from the very first idea to the time it is delivered to users.

In software and system development, this involves making privacy measures a core function of the product or process. However, it applies to more than just that; it also means thinking about privacy and data protection when designing new business systems and processes.

This approach ensures that personal information is kept secure from the moment it is collected, through to when it can be discarded after it has served its purpose. It also ensures that data is accurate and is not disclosed without consent.

As the name suggests, privacy by design is based on the idea that if a company values privacy then it will make it part of its culture. This requires a change in mindset, from one that views privacy as a tool to be added on later to a culture that sees it as a responsibility that must be taken seriously and implemented at every stage of the project.

The concept was proposed in the 1990s by Ann Cavoukian, former Privacy Commissioner of Ontario. It has since become a recognized best practice, supported by privacy authorities around the world.

In 2010 regulators at the 32nd Conference of Data Protection and Privacy Commissioners adopted a Resolution recognizing Privacy by Design as an essential component of fundamental privacy protection. It urged data protection authorities to foster this practice in the formulation of policies and legislation within their jurisdictions.

According to Article 25 of the GDPR, organizations must implement privacy by design and by default at appropriate points in the product development cycle. This will ensure that data is protected, including by implementing appropriate security measures and using pseudonymisation, where possible.

This is an important step, as the GDPR places great emphasis on protecting user privacy. It should be a top priority for any organization that collects user data.

Security by design

Security by design (SbD) is a development strategy that encourages the development of robust cybersecurity controls at the earliest stages of software development. It reduces the risk of a data breach and ensures that sensitive corporate data remains safe from cyberattacks, helping to strengthen trust in a company’s systems and information.

Secure by design is a quality-driven, layered approach to software development that integrates security into every aspect of a project’s life cycle. It is an alternative to traditional approaches that focus on a narrow set of technical and organizational measures, such as vulnerability assessments and penetration testing.

A key component of secure design is the principle that software should be secure by default. This means that it should limit access to resources until users have obtained permission to use them. Often, this is done by requiring the user to change their password or create stronger ones.

Another security by design principle is the in-depth defense concept, which emphasizes implementing multiple layers of defensive mechanisms to protect against potential breaches. This can be accomplished by implementing the principles of authentication, encryption, and integrity.

Some security by design practices include preventing unauthorized access to certain resources, limiting access to specific areas and restricting the amount of data allowed for users. It also involves implementing strong passwords and requiring user input to verify their identity.

These security by design practices protect users’ privacy and data while at the same time ensuring that an application can function efficiently. They can help to prevent hackers from getting into a system and exploiting vulnerabilities, which could lead to financial loss or damage of a company’s reputation.

In addition, a secure design can ensure that data is protected by ensuring that it is not stored in untrusted locations or exposed to the public. This can be achieved by securing storage in the cloud or by providing secure backups.

Some security by design practices also include ensuring that personal data is handled in a way that is consistent with privacy laws and guidelines, such as the European Data Protection Directive. It can also include establishing processes for protecting personal data and conducting security audits of the company’s IT system.

Transparency by design

Transparency is a key component of data privacy & protection. This is because transparency provides people with information about how their data is collected, processed, and used. It also enables them to take control of their personal data, which can be beneficial for consumers and businesses alike.

Transparency should be a core feature of the design of all data processing activities. For example, it should be embedded in the decision-making process of an automated or intelligent system. This way, the system can be examined and verified by a third party, thus ensuring that the decisions it makes are fair, verifiable, traceable, and intelligible.

In addition, it should be able to convey the logic involved in the decision-making process, which can help users understand how their data is used and why. This kind of transparency can be achieved through the use of machine-readable standardized icons that provide information about the type of data being processed and its intended purpose.

Despite the promise of transparency, there are still many ways in which data processors can go wrong when it comes to protecting privacy. For instance, they can fail to deliver timely privacy notices, fail to inform the public about how their data is being used, and may even mislead the public about how their data is used.

However, many of these errors can be avoided by designing systems that are based on the principles of transparency. This can be done by embedding these principles in the design of all data processing activities, instead of adding them as a afterthought. This approach is in line with the data protection by design and default (PbD) enshrined in Article 25 of the GDPR, which aims to minimize risks by focusing on effective ex ante protection embedded early on in the design of processes, as opposed to exclusively devising ex post remedies.

This is particularly important for AI and autonomous systems, as they are often complex, distributed, and unsupervised, and therefore can be difficult to control. Furthermore, these systems are likely to be prone to human error and can have different outcomes when they are transparent or not.

Accountability by design

Accountability by design is an important data privacy and protection principle that requires controllers to integrate or ‘bake in’ certain considerations into their processing activities and business practices from the design stage and throughout the lifecycle. It is a key focus of European Data Protection Law, which requires that ‘data protection by design and by default’ be considered at all stages of processing.

This can be a difficult concept to grasp for many organisations, but it is not impossible to do and can be particularly relevant to technology research. The results of technology research, such as self-driving cars and’smart’ cities, are reshaping entire societies across the world, and it is only reasonable to want those conducting the research to be held accountable for the impact that their work has on society.

The first step in ensuring accountability by design is to consider the nature of the research and how it is conducted. For example, how decisions are made and how they are communicated to the outside world. By identifying these, it is possible to create a framework for more accountable scientific research.

It is also important to take into account the ethical aspects of such research. For example, it is essential to provide the necessary procedural safeguards, such as ensuring that research is submitted to an ethics committee and that researchers have their data rights protected.

In addition to this, the research platform should include technical safeguards such as end-to-end encryption of research data. In this way, the researcher can be ensured that their data is only available to those they consent to share it with.

Similarly, accountability by design can be achieved through the development of fair information practice principles (FIPs), a common set of requirements across all areas of privacy policy and practice that is grounded in external criteria. This enables organizations to express these core principles, which can help to reassure individuals, business partners and regulators that their policies and practices comply with privacy laws.

About Author