Prioritize Privacy Enhancing Computation Technologies To Fight Back Vulnerabilities
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Prioritize Privacy Enhancing Computation Technologies To Fight Back Vulnerabilities

Prioritize Privacy Enhancing Computation Technologies To Fight Back Vulnerabilities



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Before jumping into the details of privacy-enhancing computation, ask yourself: have you ever thought twice before providing your personal information to any application or website? Has this thought ever struck your mind that this probably is unsafe to provide your details? Surely, you have been through this thought at least once in your lifetime. This is normal because even though modern-day technology has almost skyrocketed, the probability of hacker attacks and data breaches is evolving simultaneously at a swift pace. 

The Identity Theft Resource Center found that year-over-year results indicate a fast start to data breaches in 2022 as more than 90% of the data breaches are cyber attack-related. On top of that, researchers have discovered 88 publicly disclosed cybersecurity incidents, accounting for 3,987,593 breached records. This leads to the tidal number of leaked records to 75,099,482 during the first half of 2022. Woah! Such great numbers. 

So now the question arises, what’s the solution to this non-negligible problem? None other than PETs. So without any further delay, keep reading to know more about this technology. 

What Are Privacy Enhancing Computation Technologies? 

As the name suggests, privacy-enhancing technologies (PETs) are a vast range of technologies that include both hardware and software solutions. Such solutions are designed for the extraction of data value to unleash its full scientific, commercial and social potential, without risking information’s privacy and security. 

PEC permits different parties to extract value from the data and attain actionable results from it without even sharing data with those third parties. This is the most effective way to collaborate without risking sensitive data. 

Privacy-enhancing computation is a tech trend that can take three forms as follow: 

  • Offering a secure environment for data processing.
  • Utilization of privacy-aware machine learning for analytics.
  • Encryption of data to keep the original data confidential.
Homomorphic Encryption

Now, let’s lay our eyes on the foremost privacy-enhancing computation technologies: 

  • Homomorphic Encryption:  Homomorphic encryption is the most secure option in privacy-enhancing computation. This technology processes the data but also keeps it confidential. The user never gets to know the original data because he applies functions to the encrypted data. 
  • Secure Multiparty Computation: Secure multiparty computation is a type of technology that allows multiple parties to operate sensitive data together while keeping their own individual inputs secure to combat illicit activities.  
  • Differential Privacy: This technology is a kind of algorithm that computes and analyses statistics on a data set. After analysis and computation, it provides a general dataset while describing the patterns. This algorithm does not disclose the information of individuals.  
  • Trusted Execution Environments:  This least secure technology is part of the main processor where data is stored, processed, and protected in a secure manner. This technology is used in most modern digital devices such as tablets, smartphones, etc. 
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