Privacy-enhancing computation ensures that one party cannot get access to private data from another party. This allows more than one party to collaborate, perhaps to monetize, on data without revealing the data to each other. The data stays encrypted but usable, reducing the risk of leakage or theft. Privacy-enhancing computation is big business. Gartner reports that within two years, 50% of big companies will start using privacy-enhancing computation as part of their data protection strategy. Specifically, these businesses plan to use privacy-enhancing computation to work with data in multiparty data analytics and in untrusted environments.
Companies walk a double-edged sword when it comes to privacy-enhancing computation. According to Gartner, it is a complicated process, and many businesses might wait years to see their efforts bear fruit. But, companies that don’t take steps to protect their data run the considerable risk of being compromised by cyberattacks. And data breaches come with a huge price tag, financially, legally, and to their reputation. Learn how companies can protect themselves with privacy-enhancing computation.
What is Privacy Enhancing Computation?
It’s a good idea to think of “privacy enhancing computation” as an umbrella term for a wide range of technologies that all work together to support the highest level of privacy protection or private data conservation. Any technology that promotes privacy and keeps data safe from hacker attacks, breaches, or infringements can be used and regulated by privacy-enhancing computation. In addition to security and protection, privacy-enhancing computation allows data to be freely shared without interruptions while maintaining security.
How Does Privacy Enhancing Computation Work?
Privacy-enhancing computation uses many technologies, including anonymous credentials, enforcement of data handling conditions, data transaction logs, and limited disclosure technology. These technologies protect from privacy breaches and support self-sufficiency in analytical understanding. Here are two examples of common privacy-enhancing computation technologies.
Data Masking
Data masking is commonly used to protect sensitive data while creating a false but functional alternative version of that data. Data masking changes the values of the data while keeping its original format, and its goal is to create a version of data that, while usable, cannot be reverse-engineered. Data masking incorporates techniques like encryption, character shuffling, and character or word substitution. One benefit of data masking is that the data is useless in the event of a breach. This technique is often used when a company migrates to the cloud.
Cryptographic Algorithms
One common cryptographic algorithm used in privacy-enhancing computation is homomorphic encryption. Like data masking, homomorphic encryption results in data that can be used as if it weren’t encrypted. Specifically, complex mathematical operations can be performed on homomorphically encrypted data and yield the same results as if they had been performed on unencrypted data. Homomorphic encryption is used to change sensitive data into a set of data that still retains a relationship to the original set. This technique is excellent for data that needs to be used in a third party or another untrusted environment.
Benefits of Privacy Enhancing Computation
1. Data is protected
Harm from data breaches is prevented with privacy-enhancing computation. Data breaches are costly, with the average price tag in the millions. This is to say nothing of the potential damage to reputation, which can be deadly to a company. As the adage goes, customer trust is far easier to break than it is to build. Part of the cost of a data or privacy breach is in any downtime a company faces resulting from that breach, which can also carry a steep price tag. Add to this cost any fines due to data protection and privacy laws and the potential for litigation, and it’s easy to see how devastating a breach can be for the modern company.
2. Cloud protection
As more and more companies migrate to the cloud, securing cloud data is becoming increasingly essential. Privacy-enhancing computation reduces the risk of data exposure by keeping data secure during runtime or processing.
3. Data retains its usability
Data can be used while remaining secure. This allows companies to use and monetize their data while retaining its security.
4. Invisibility
Privacy computing networks are not visible to hackers (or cloud providers) and can not be tracked.
5. Increased innovation
Companies that work with privacy-enhancing computation create opportunities for their Product and Engineering teams to think outside of the box and explore new approaches to problems. This can lead to the creation of new, more secure products.
Privacy Enhancing Computation with Encora
Encora’s teams of software engineers have experience integrating the full spectrum of privacy-enhancing computation techniques and technologies into every phase of the software development lifecycle (SDLC). Companies can leverage their partnership with Encora to ensure data security while retaining usability through Encora’s skill and experience with privacy-enhancing computation. Reach out to Encora today with any questions or to get started!
