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Anonymesh: How can it ensure data privacy?
Privacy-enhancing technologies (PETs) are a set of techniques that safeguard sensitive data while keeping it useful for your business. They go beyond basic encryption, focusing on minimizing data collection and boosting security.
PETs make it possible for companies to analyze personal data in a way that doesn't compromise privacy.
What is it?
Techniques that safeguard personal data, enabling its use while protecting individual privacy and ensuring compliance.
What’s in it for you?
Enhanced data security, reduced risk of breaches and improved compliance with privacy regulations.
What are the trade-offs?
PETs increase complexity and costs. They can also reduce the utility of your data.
How is it being used?
They’re useful in highly regulated sectors and for those looking to deliver personalized services without compromising privacy.
What are privacy-enhancing technologies?
Privacy enhancing technologies (PETs) are a set of techniques designed to safeguard sensitive data while enabling its continued use by businesses.
Examples of PETs include:
Trusted execution environments (TEEs), which provide a secure environment for code and data to operate in, isolating them from the main processor and memory.
Secure multi-party computation, which allows multiple parties to collaborate on data analysis without revealing their underlying data to each other.
Federated analytics, which allows AI models to be trained across distributed datasets, eliminating the need to centralize sensitive individual data.
Homomorphic encryption, which allows computation to be done directly on encrypted data without it needing to be decrypted.
Differential privacy, where carefully calibrated "noise" is introduced into a dataset, to ensure individual anonymity while preserving the integrity of aggregate analysis.
PETs empower businesses to extract valuable insights from data and deliver enhanced services without compromising customer privacy.
What’s in it for you?
PETs primarily build and reinforce customer trust and loyalty. In an era of heightened data privacy concerns, demonstrating a proactive commitment to safeguarding personal information can differentiate a business and strengthen relationships with customers. They also help reduce regulatory and reputational risk: by minimizing data exposure and enhancing security, they significantly lower the likelihood of costly data breaches and non-compliance fines.
Beyond mitigating reputational and legal risks, PETs can also open up new opportunities. For instance, they can help businesses derive valuable insights from sensitive data that would otherwise be inaccessible or too risky to use — in turn, this can lead to improved product development, more effective marketing, and better strategic decision-making.
What are the trade-offs of privacy-enhancing technologies?
Implementation complexity. Integrating PETs into existing systems often requires specialized expertise and can be a resource-intensive process, demanding significant upfront investment in development and training. This complexity can also lead to longer deployment times.
Computational overhead. Many PETs, like homomorphic encryption, involve complex cryptographic operations that can be computationally intensive, leading to slower processing times and potentially higher infrastructure costs. This can impact the performance of applications that rely on real-time data processing.
- Potential reduction in data utility. While PETs aim to preserve utility, some techniques can lead to less precise insights or limit the scope of analysis; this will make it harder to extract all the potential value from your data. Striking the right balance between privacy and utility is crucial.
How are privacy-enhancing technologies being used?
In healthcare PETs are helping with things like secure research and analysis — where techniques like federated learning are used to enable collaboration across different institutions while protecting patient data. They’re also useful in improving the delivery of care; anonymized patient data, for instance, can be more easily transferred across different providers.
In financial services PETs are used for things like fraud detection, where secure multi-party computation allows different financial institutions to collaboratively analyze transaction patterns. They can also be used for credit scoring, helping banks leverage data from different sources,
In advertising and marketing PETs can help deliver privacy-preserving targeted advertising and to more effectively measure campaign performance and audience engagement without compromising consumer privacy.
PETs are also useful in government and public sector contexts, where privacy is particularly important. It’s helpful, for instance, for things like statistical analysis, where techniques like differential privacy can be used to add noise to a data set to protect citizens’ information. It can also enable more effective and secure data sharing. While working with public sector organizations in the UK, for instance, we developed a PET technique called anonymesh — it helped to facilitate more secure data sharing across public sector organizations.
