Data safety is an increasingly important, complex and nuanced topic in the world of EdTech.

One of the simplest steps any organisation can take, whether in education or industry, is towards processes that promote data minimisation.

Data minimisation is a fundamental principle of data privacy, emphasising the importance of collecting and retaining the personal data of users that is deemed necessary.

Schools, organisations, and software providers can significantly reduce the risk of data breaches, and non-compliance penalties, and ensure regulatory compliance by limiting the amount of data processed and retained on systems.

Simply put, when an organisation collects and stores less data, it diminishes the potential for data mishandling, whether intentional or accidental. This proactive approach helps protect sensitive information and minimises the impact of any potential security issues.

By adhering to data minimisation principles, organisations can demonstrate their commitment to data privacy and build trust with their customers and stakeholders.

Why Data Minimisation Matters

In your personal capacity, as teacher or leader in a school environment, or at an organisation, data minimisation practices can have a significant impact on the baseline level of safety of online activities.

Data minimisation is a fundamental principle of data privacy and security. It emphasises the importance of collecting and processing only the absolute minimum amount of personal data necessary to achieve a specific purpose. 

This approach offers significant advantages for both individuals and organisations.

For Individuals:

• Reduced Risk of Harm: By limiting the amount of personal data collected, the potential impact of a data breach is significantly minimised. Less data in the hands of malicious actors reduces the potential for identity theft, financial fraud, and other forms of harm.
• Enhanced Privacy Control: Data minimisation empowers individuals with greater control over their personal information. When only essential data is collected, individuals have a clearer understanding of what information is being processed and how it is being used. This transparency fosters trust and allows individuals to make more informed decisions about how their data is shared.
• Protection from Unnecessary Surveillance: In an increasingly data-driven world, data minimisation helps to prevent excessive data collection that could be used for purposes beyond the initial intent. This safeguards individuals from unwarranted surveillance and the potential for their data to be used in ways they did not consent to.

For Organisations:

• Reduced Risk of Data Breaches: Significantly lowers the impact of risk of data breaches.
• Stronger Security Posture: By collecting less data, organisations reduce their attack surface. This makes it more difficult for hackers to identify and exploit vulnerabilities, strengthening the overall security of the organisation's systems and data.
• Enhanced Data Governance: Data minimisation aligns with best practices in data governance. By focusing on collecting only necessary data, organisations demonstrate a commitment to responsible data handling and compliance with relevant regulations.
• Reduced Operational Costs: Managing large volumes of data can be expensive and resource-intensive. Data minimisation helps organisations to reduce storage costs, improve data processing efficiency, and streamline data management processes.
• Increased Trust and Reputation: By prioritising data privacy and security through data minimisation, organisations build trust with their customers and stakeholders. This can enhance organisational reputation and foster long-term relationships.

Implementation Strategies and Best Practices

Implementing data management strategies and best practices plays a foundational role in mitigating the risk impact of collected data. 

Data Minimization by Design

Adhering to data minimisation principles is required by many data protection regulations, and doing so from a perspective of design helps to ensure that processes are conceived of and implemented throughout. 

Seeking to integrate further principles in addition to data minimisation from a design perspective sets up a foundational approach to ensuring that the risk impact of collected data is minimised. 

A notable example of principles to consider are those from the General Data Protection Regulation (GDPR) set out by European law. 

By considering and implementing such processes and only collecting data deemed necessary, organisations may take another step to demonstrate compliance with legal and regulatory obligations.

The following GDPR principles are three key principles to consider in addition to Data Minimisation:

• Lawfulness, Fairness and Transparency: Personal data should be processed in a transparent manner which is legal to ensure fairness for the individuals whose data is being processed. 
• Purpose Limitation: Personal data should be collected for specific, explicit, and legitimate purposes, and should be communicated as such to individuals  - and not processed in a manner incompatible with those stated purposes.
• Storage Limitation: Personal data should be kept for no longer than is necessary for the purposes for which the data was collected.

A more indepth breakdown of these principles is available here to further a proactive approach to help maintain a strong regulatory compliance posture.

Collect Only Necessary Data

Before collecting any personal data, organisations should carefully identify the specific purposes for which the data is needed.

Only the minimum amount of data required to achieve these purposes should be collected. This principle ensures that data collection is focused and avoids unnecessary data accumulation.

Purpose Limitation

Data should be used solely for the intended purposes for which it was collected.

Organisations should avoid using personal data for secondary purposes without obtaining explicit consent from the data subjects, and in a lot of cases even with customer understanding unless there are clear benefits for the customer, this should be avoided.

This helps to maintain data privacy, prevent misuse, and helps K12 institutions retain trust.

Data Retention Policies

Clear and concise data retention policies should be established to determine the appropriate duration for data storage.

Data should be regularly reviewed and promptly deleted or anonymized once it is no longer needed for its original purpose. Data retention periods should be established and enforced to ensure the timely removal of unnecessary information.

This helps to minimise the risk of data breaches and dire fallout resulting from unauthorised access.

Data Minimisation Impact Assessments

Regular assessments should be conducted to identify opportunities for further data minimisation and evaluation of the data held.

These assessments can help organisations identify and eliminate unnecessary data collection practices, ensuring that only the essential data is retained.

Furthermore, these processes are important for staying on top of the best practices carried out in regards to data collection and minimisation.

Technical Measures

Beyond best practices at an operational level with K12 institutions, ensuring that the required technical measures are taken into account is of great importance.

Some of the technical measures that may be carried out are:

• Data Masking
• Tokenisation 
• Anonymisation 
• Encryption 

Data Masking

Data Masking, also referred to as Data Obfuscation, is when sensitive data is modified to prevent the identification of the sensitive information. 

This can be done by replacing sensitive information with non-sensitive placeholder values, resulting in useless or dumb data. This technique, known as data masking, can be used to conceal critical information while preserving the overall structure of the data. 

For example, a credit card number might be masked to reveal only the first few digits and the last four digits, hiding the sensitive middle digits. 

Tokenisation 

Sensitive data can be replaced with unique tokens that can be linked back to the original data to increase the complexity of external parties understanding the data within a system. 

Tokenization involves replacing sensitive information with a non-sensitive equivalent. This technique can be used to protect sensitive data while still allowing it to be processed and analysed.

Anonymisation 

Personally identifiable information can be removed from data to create anonymized datasets. 

Anonymization techniques can involve removing or modifying specific data elements, such as names, addresses, and social security numbers, to make it difficult to identify individuals.

Encryption

Sensitive data can be protected by transforming it into an unreadable format. Encryption involves using cryptographic algorithms to scramble data, making it inaccessible to unauthorized individuals. 

Strong encryption algorithms can significantly enhance the security of sensitive information, both at rest and in transit.

Ideally, organisations should implement encryption processes for data at both rest and in transit, to ensure that data remains safe and as inaccessible as possible to external parties.

A Case Study: Healthcare Provider Data Minimisation

A leading healthcare provider, Kaiser Permanente, has been a pioneer in implementing data minimisation practices. 

When patients visit their clinics, they collect only the essential medical information directly relevant to the specific visit. This includes symptoms, medical history, and current medications. 

By avoiding unnecessary data collection, such as detailed family medical history or genetic information that isn't immediately pertinent to the patient's current health concern, Kaiser Permanente reduces the risk of data breaches and ensures patient privacy. 

Additionally, they employ advanced data anonymisation techniques to protect patient identities while still enabling valuable research and analysis. 

This approach not only complies with data privacy regulations but also fosters trust between the healthcare provider and its patients.

A Stronger, More Secure Digital Future

The increasing complexity of the digital landscape necessitates a proactive approach to data privacy and security. Data minimisation emerges as a powerful strategy to protect sensitive information and mitigate risks. 

By limiting the collection and retention of personal data, individuals and organisations can significantly reduce their exposure to cyber threats, data breaches, and regulatory penalties.

For individuals, data minimisation empowers them to take control of their digital footprint. By being mindful of the information they share online and choosing privacy-focused settings, individuals can enhance their online privacy and security. 

For organisations, data minimisation is essential for building trust with customers, complying with data protection regulations, and safeguarding their reputation. By implementing robust data minimisation practices, organisations can create a more secure and resilient digital environment.

As technology continues to evolve, it is crucial to prioritise data minimisation as a fundamental principle of digital security. 

By adopting best practices and staying informed about emerging threats, we can collectively work towards a stronger, more secure digital future. 

Let us embrace data minimisation as a powerful tool to protect our privacy and safeguard our digital world.

Onwards, 

Panashe Goteka

Team Mobile Guardian