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Privacy-Enhancing Computation

privacy-enhancing-computation

Your data is valuable, and you don’t want to share it with just anyone. The solution? Privacy Enhancing Computation (PEC). It is a way for different parties to extract value from information without exposing themselves or their other datasets in return. They collaborate on an individual level using the actionable data without any shared sensitive information between the participants.

Personal data, like your bank account number or social security number, are not available to the public. The way that privacy-enhancing computing creates private datasets for individuals is by using strong encryption methods in combination with other technologies.

Several techniques combine to make up the privacy-enhancing computation. These may be included:

Zero-Knowledge Proofs

Zero-knowledge proof, or zero-knowledge protocol, is used when one party shares true information but only shows what is true without revealing anything else about it.

Multi-Party Computations

Secure Multi-Party Computation (SMC) has the goal for people to work together in computing functions over their inputs without revealing them individually. There is shared computational operations among all parties, but no single party can know what was done by any other at any time during processes.

Homomorphic Encryption

Homomorphic encryption is a new way to protect data that can be used in computing. Parties perform computations on encrypted information that remains all the while encrypted.

Differential Privacy

Differential privacy is a system that allows information about datasets to be shared while still protecting the identities of individual members in each group.

Trusted Execution Environments (TEE)

A trusted execution environment is a secure area of your main processor that ensures code and data loaded inside are protected with respect to confidentiality, integrity.

Privacy-Enhancing Computation Examples

Here are some key uses for Privacy-Enhancing Computation (PEC):

HR

The use of PEC in the Human Resources Department can be in facilitating gender equality and reducing the gender pay gap in the workplace.

Fraud Prevention

Fraudsters are known to victimize certain industries and multiple companies in that industry. Companies can work directly together using PEC to detect the criminals quickly. Also, the good customers can be identified when they collaborate to establish a pool of trusted consumers.

Medical Research

In a pandemic year, it is understandable why the medical communities need to draw large amounts of data, even across borders and laws, for research. Patient records are rightly protected by many regulations. The PEC process makes actionable patient information both accessible and private.

Internal Data Analysis

PEC methods can help large corporations obtain and share information, even between many brands and across borders and policies, while maintaining regulated privacy.

Privacy-Enhancing Computation Benefits

· Ever-Tightening Protected Data Can Be Safely Accessed

Technology leaders of organizations can be spared the difficulties of ever-tightening privacy laws while still being able to access most of their data. PEC methods can make the most of data availability better than the ineffective “anonymizing” method.

· Consumer Data Need Not Be At Risk

By using PEC techniques, consumers can be spared the potential risk of violated privacy due to ineffective methods of shared information with business needs.

Conclusion

Every day, the data gathered from social media to bank accounts on the Web continues to grow enormously. When consumers provide their personal information for goods and services, they expect their information will be protected by these organizations. They want to remain anonymous. But companies want actionable business information to help grow their businesses. Fortunately, there are methods to make use of all this consumer data in secure ways with the techniques of Privacy-Enhancing Computing (PEC).

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