Privacy Enhancing Computation is essential to Secure Data from Cybercriminals

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Data is currently the most important asset and a key concern to all businesses. The highest priority for most companies is to keep data privacy & security. There are privacy regulations all around the world because privacy breaches happen randomly with new techniques. However, the involvement of 3rd parties in business transactions is one of the major reasons behind cybersecurity incidents.

However, 3rd parties use the data to gain perceptions in improving their services or just to earn additional money. Consumers are now more concerned regarding sharing their personal data. Attackers are using more complicated methods to access data. So, the use of PEC (Privacy-enhancing Computation) and PET (Privacy-enhancing Technology) has become more essential security mechanisms such as AI.

Do You Know Privacy Enhancing Computation?

There still isn’t any standard definition but the privacy-enhancing computation is designed at controlling a group of different technologies to provide more powerful protection for private data. This technology-equipped group offers data protection and privacy support. It also gives significant protection against cyberattacks and data breaches.

However, there are various hardware and software solutions aimed at gathering precious data. The collected data is used for many purposes in providing a secure and strong foundation. These technologies are now used for real-life applications. Gartner has described PEC as the People Centricity Category. A research company said PEC has 3 classifications featuring 3 technologies to secure data.

Three Key Classifications of PEC

The first form of the technology offers a trusted environment to securely process the data. Meanwhile, there are trusted 3rd parties and the execution of hardware trusted environments to enable this.

The 2nd form provides analytics and processing over privacy-aware machine learning. This form of utilized technology features is associated with machine learning and privacy awareness machine learning.

The 3rd form of PEC is an effective technology to enable the transformation of data and algorithms. These algorithms include Homomorphic encryption in keeping the data private. However, differential privacy, private set intersection, and multiparty computation are some of them.

The Implementation and Benefits of PECs

The following are some of the key benefits to enable privacy-enhancing computation.

Prevention against Harmful Cyberattacks

Cybercriminals can easily gain access to information without implementing protection measures to prevent privacy data breaches. There are various types of cyberattacks involving bank details, social media accounts, cloud stores, and many others. A successful data breach can compromise the user’s privacy and disturb their lives. PECs are used to secure sensitive information and ensure the enabling of a mandatory set of permissions.

Tracking Obscure and Discriminatory Conditions

It is hard to track the 3rd party provider’s activities for how they use the customer’s sensitive data and information. However, there are terms & conditions in the privacy policies. But there isn’t any mechanism to ensure the actual implementation of policy rules.

Prevent Misrepresentation for Possibilities

Personal data exposure can endanger sensitive data and attackers use it to abuse individuals. They can make modifications or changes such as publishing representing another user. PEC controls such alteration of data and prevents affecting the genuineness of the actual user. It protects the identity and interest of a user, while the data is used for misrepresenting or other different purposes.

Human Dignity Violation Prevention

The lack of privacy can present a perfect stockpile for users with malicious focused attempts. Cybercriminals misuse information and alter the views, or decisions of the actual user. It creates critical issues such as the violation of dignity and misjudgments of people in real life. PECs can efficiently prevent such issues and situations.

Techniques involved in Privacy Enhancing Computation

Zero-Knowledge Protocol

ZKP, Zero-Knowledge Password Proof, or Zero-Knowledge Protocol feature verifications without exchanging passwords. This method uses only true shared information without disclosing other elements in making more secure communication.

Securing Multiparty Computations or SMC

SMC or Secure Multiparty Computation is a communication security protocol. It enables users to collaborate on different computing functions through their inputs without individually disclosing them. Users can analyze multiple data without illegal privacy.

Homomorphic Encryption Technology

This technology allows the processing of encrypted data for 3rd party providers. It is a new method to secure data by sustaining data confidentiality during processing. However, private data used in different sectors according to requirements including medical and banking is processed without requiring private information. Only specific users can unencrypt data and use specific keys to access it.

Differential Privacy Technique

Differential privacy is an algorithm that allows information regarding the available set of data to share. This data is shared without disclosing the identities of individual users in each group. The algorithm in the differential privacy method ensures the security of private data.