Privacy computing is an emerging area that extends the current concepts of data privacy and protection. We discuss what it is, how it differs from data protection, and why organisations ought to take heed of this important development.
Increasingly, we, the consumers and users of the internet and digital services, are in a Catch-22. To access services we want or need, we are often required to share personal information, such as our name, date of birth, email address, home address, and identification number, to name just a few. At the same time, the value of data has increased, which has resulted in even more data being collected via the devices and software applications we use. Moreover, that data is stored, commoditized and exploited in ways often unbeknownst to the original owners.
However, as history has shown us, all of that personal data is continually at risk of being compromised. As a result, there has been a growing focus on privacy computing, cognisant that the risk of leaks of individuals’ personal information is not going away, but increasingly stringent privacy and data protection rules have been emerging.
What is privacy computing?
Privacy computing refers to a range of technologies and practices that prioritise the protection of individuals’ privacy and data while using digital services and technologies. Essentially, it focuses on sharing the value of data without sharing (or risking the privacy and security of) the underlying data itself.
Privacy computing covers a broad range of approaches and disciplines, including secure data processing, data anonymisation, cryptographic techniques, artificial intelligence and decentralised architectures, to minimise data exposure whilst maximising control over personal information. Typically, the approaches and techniques used seek to facilitate three objectives:
- To create a trusted environment in which sensitive data can be processed or analysed.
- To encrypt data and algorithms, thus fostering privacy protection and security of users’ data, before it is processed or analysed.
- To allow powerful computational processing and analytics to be performed in a decentralised manner.
How is privacy computing different from data protection?
As a sidebar, but noting the prevalence of data protection systems in organisations, the difference between data protection and privacy computing may not be readily evident.
Data protection tends to encompass measures and practices to safeguard data from unauthorised access, use, disclosure, alteration, or destruction. It thus focuses on matters related to securely storing data, implementing access controls, and establishing data breach response plans. Accordingly, it is concerned with securing data against various risks, including cyberattacks, unauthorised access, and accidental loss, and with maintaining data integrity, availability, and confidentiality.
On the other hand, and as previously stated, privacy computing, specifically concentrates on protecting individuals’ privacy while using digital services and technologies. Hence, although privacy computing is also concerned with data protection, it has a more specific objective of preserving individuals’ privacy rights by addressing the risks associated with data collection, processing, and profiling that can infringe on individuals’ privacy and autonomy.
Why is privacy computing important?
With data being the new oil, and essential to providing us with ultra-personalised services and facilitating the continued development of advanced technologies, such as artificial intelligence, there will be no rollback on the collection and use of individuals’ private information, Instead, privacy computing seeks to offer a win-win situation: permitting that data to be used without compromising individuals’ privacy and the security of their data. Hence some of the perceived benefits include the following
- Protecting sensitive data: Privacy computing safeguards individuals’ sensitive data, including financial records, health information, and personal communications. By implementing privacy-enhancing technologies, individuals’ personal data would be safeguarded, thereby reducing the risk of unauthorised disclosure or exploitation.
- Mitigating surveillance risks: With the proliferation of surveillance technologies, there are growing concerns about mass surveillance and unwarranted monitoring. However, in employing encryption, anonymisation, decentralised architectures and other techniques, privacy computing mitigates these risks by making it more difficult for surveillance entities to access and analyse personal data.
- Encouraging trust in digital services: Privacy breaches and data mishandling incidents have eroded public trust in digital platforms. Privacy computing is seen as offering a solution by prioritising transparency, accountability, and user-centric design. When individuals have confidence that their privacy is respected and their data is being handled responsibly, they are more likely to engage with digital services, which is crucial for the digital transformation of our societies that has been occurring.
In summary, in an era marked by increasing emphasis on data generation, collection and analysis, privacy computing is emerging as a fundamental pillar for safeguarding our personal information and preserving our digital autonomy. It not only mitigates surveillance risks but also fosters trust in digital services and enables ethical data practices.
Having said this, privacy computing is still in its infancy. Industry-wide standards, compliance models and best practice are not been adopted. However, the market is growing exponentially, and depending on the source, its value could be over USD 100 billion by 2026. It is thus an area to which organisations ought to pay attention – especially if they want to be able to adhere to the data protection rules whilst leveraging the data they have in their possession.
Image credit: Towfiqu barbhuiya (Unsplash)
