Every year, during Black Friday week, we at SSLs.com like to celebrate all things security while offering you some great deals (see: our current ‘buy 2, get 1 free‘ promo). This year is all about PETs.
Much like SSL, our pets can help us feel safe and secure in our everyday lives. But beyond our little fluffy friends, there’s also PETs – privacy-enhancing technologies. Let’s explore what they are and what they do for you.
What are PETs?
PETs are tools, technologies, and practices designed to protect personal data in various ways. Some PETs focus on anonymization, while others enable analysis on private datasets, without exposing copies of the data. Other methods include encryption, anonymization, access controls, and confidential computing.
Why PETs?
As the world and digital sphere become increasingly data-centric, with stories of data breaches becoming commonplace, PETs help private individuals and organizations maintain control over their data and mitigate privacy risks. Beyond personal data, PETs can also safeguard sensitive details such as intellectual property and trade secrets..
These technologies create an environment of trust, where everyday people feel safer online, knowing their data will be kept safe, reducing the risk of surveillance, fraud, and data theft, and increasing trust in online companies. For organizations, PETs make it easier to adhere to the stricter privacy regulations being implemented worldwide and avoid fines and reputational damage.
Examples of PETs
A wide range of PETs exist to keep data safe and secure. Here are just a few examples:
Differential privacy
Differential privacy is a mathematical data analysis method that uses aggregation to add randomized “noise” to data. This noise makes it impossible to reverse engineer and understand the original inputs. It produces a summary of generalized data, enabling meaningful analysis while preserving privacy. Practical use cases include analyzing consumer behavior and census data.
Homomorphic encryption
Homomorphic encryption enables analysis of encrypted data at rest, in transit, and in use, without decryption. It balances maintaining data privacy with gleaning essential insights. Practical use cases include financial calculations and secure data analytics in areas such as digital advertising.
Federated Learning
Federated learning involves training models across multiple decentralized devices using local data samples. The model becomes smarter with every data analysis. Raw data is never sent to a central server. It only receives model updates, ensuring data privacy is maintained. Practical use cases include healthcare AI and mobile AI.
The takeaway
Sometimes it can seem like the online world is full of bad actors out to steal our data, but PETs, fortunately, exist to reduce the likelihood of that happening. So don’t forget to celebrate PETs, pets, and take a look at the ongoing buy 2, get 1 free sale.
Cora is a digital copywriter for SSLs.com. Having eight years of experience in online content creation, she is a versatile writer with an interest in a wide variety of topics, ranging from technology to marketing.