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Trends in Security Information
The HSD Trendmonitor is designed to provide access to relevant content on various subjects in the safety and security domain, to identify relevant developments and to connect knowledge and organisations. The safety and security domain encompasses a vast number of subjects. Four relevant taxonomies (type of threat or opportunity, victim, source of threat and domain of application) have been constructed in order to visualize all of these subjects. The taxonomies and related category descriptions have been carefully composed according to other taxonomies, European and international standards and our own expertise.
In order to identify safety and security related trends, relevant reports and HSD news articles are continuously scanned, analysed and classified by hand according to the four taxonomies. This results in a wide array of observations, which we call ‘Trend Snippets’. Multiple Trend Snippets combined can provide insights into safety and security trends. The size of the circles shows the relative weight of the topic, the filters can be used to further select the most relevant content for you. If you have an addition, question or remark, drop us a line at info@securitydelta.nl.
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Privacy techniques are measures an individual or organisation can take to improve the protection of their personal data. These techniques can range from adopting certain behaviours to technical measures. Some examples of behavioural privacy techniques are to: not store private data on public storage services, not use public Wi-Fi-networks for sending sensitive data and create strong passwords for accounts. Technical privacy techniques include: (homomorphic) data encryption, secure multi-party computation, webbrowsers blocking certain trackers, access control, multi-factor authentication, VPN and onion routing. Privacy Enhancing Technologies (PET) increase control over personal data sent to and used by others, minimize the amount of personal data collected and used, use pseudonyms or anonymous data credentials, strives to achieve informed consent, makes it possible to remotely audit the enforcement and control the user’s data.
It is important for privacy techniques to stay forward-looking to keep data secure in the future. An example that is often mentioned is quantum computing technology being able to crack current encryption methods in the near future. But also, data analytics capabilities that combine multiple data sets to identify people based on data that by itself is not considered privacy unfriendly. Privacy engineering or privacy by design becomes more popular in IT developments nowadays to safeguard privacy from the start.
Related Keywords: GDPR, AVG, cookies, security, safeguard, biometric identity protection, Personally Identifiable Information (PII), data protection, privacy policy, pseudonymisation, self-sovereign identity
Privacy techniques are measures an individual or organisation can take to improve the protection of their personal data. These techniques can range from adopting certain behaviours to technical measures. Some examples of behavioural privacy techniques are to: not store private data on public storage services, not use public Wi-Fi-networks for sending sensitive data and create strong passwords for accounts. Technical privacy techniques include: (homomorphic) data encryption, secure multi-party computation, webbrowsers blocking certain trackers, access control, multi-factor authentication, VPN and onion routing. Privacy Enhancing Technologies (PET) increase control over personal data sent to and used by others, minimize the amount of personal data collected and used, use pseudonyms or anonymous data credentials, strives to achieve informed consent, makes it possible to remotely audit the enforcement and control the user’s data.
It is important for privacy techniques to stay forward-looking to keep data secure in the future. An example that is often mentioned is quantum computing technology being able to crack current encryption methods in the near future. But also, data analytics capabilities that combine multiple data sets to identify people based on data that by itself is not considered privacy unfriendly. Privacy engineering or privacy by design becomes more popular in IT developments nowadays to safeguard privacy from the start.
Related Keywords: GDPR, AVG, cookies, security, safeguard, biometric identity protection, Personally Identifiable Information (PII), data protection, privacy policy, pseudonymisation, self-sovereign identity