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Privacy-Enhancing computation will change the future of personal data protection

We analyzed Gartner’s top trend for enterprise security in 2022

Published: November 30, 2021 By Hamna Imran

Privacy enhancing computation: Towards secure data sharing

Image source – analyticsinsight.net

"Data is the new oil."

As we approach a new decade, it looks that data will continue to be one of the most precious things a corporation can create – and protect.

In fact, according to a recent Pew research centre poll, 79% of Americans are concerned about how firms use the data (such as IP addresses) obtained about them. Furthermore, 52% of respondents said they would not utilise a product or service because they were concerned about acquiring their personal information.

Gartner forecasts that by 2023, firms that build privacy user experience into the customer experience will have much more trust and up to 20% more digital revenue than those that do not.

It's simple: customers who have more trust spend more.

What is Privacy Enhancing Computation?

Gartner defines PEC as having three technologies that secure data while it is in use, and it falls under the category of "people centricity." Among these technologies are:

  • A secure environment in which confidential information may be handled or evaluated.
  • Processes and analyses data in a decentralised fashion.
  • Before analytics or processing, data and algorithms are encrypted.

Organisations can now conduct research safely across regions and with competitors without jeopardising their privacy due to this development.

According to Gartner, the trend of privacy-enhancing computing is challenging to implement in most companies. This procedure is lengthy since the integration must be speedy and correct.

It is interesting to note that 27 percent of internet users never provide their true personal data when enrolling on a website, app, or any other system where private information might be hidden.

The Need for Privacy-Enhancing Computation

PEC was created to meet the growing demand for data sharing while ensuring security and privacy.

Furthermore, today's organisations are seeking ways to ensure consumers and other firms in a B2B setting that whatever data they hold is secure.

More breakthroughs have been achieved in the domain of PETs (privacy-enhancing technologies), a comprehensive group of technologies that enable, enhance, and protect data privacy throughout its lifespan, as a result of growing concerns about data privacy.

Helpnet security writes.

Users want to know that their personal information will be kept private when they submit it to any website, application, or form. The suppliers of data storage must have complete control and management over this information.

Obtaining a high degree of security is no longer a difficult undertaking. The data of users will be protected thanks to contemporary privacy technologies entirely.

Need of Privacy enhancing computation techniques

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We want to emphasise the most prevalent cause for deploying data-protecting technological tools.

Harm Prevention

The lack of protection might allow anybody who wants to own information without authorisation to have simple access to it.

It may be anything from social media accounts to bank accounts to data in a cloud storage facility. It can jeopardise consumers' privacy and have a long-term impact on their life.

Unfair conditions

When users entrust their data to third-party providers, they cannot track the actions that occur with it. They do not enter any contracts; all they can do is consent to the terms and conditions or privacy policies.

They cannot, however, guarantee that all regulations are obeyed. That is why there are data protection laws and government regulations regarding its usage. Each infraction can be disputed and adjudicated upon.

Human dignity violation

Individuals' conduct might be influenced by their absence or lack of privacy. People's decisions will be influenced and skewed if they know that other users can track their information.

Most internet users do not want to stand out from the crowd by acting out of character. It leads to real-life misjudgments and violations of human dignity. All users must recognise that not all information is true, even if it appears to be personal.

Misrepresentation

When personal data is disclosed, it might be used for dubious objectives and cause harm to persons.

The initial information's meaning might be modified, for example, obtained from one location and published in another. The purpose of this data shifts, and it may be used to discriminate against individuals.

According to current research, between 70% and 89.5% of all active Internet users are concerned about the privacy and security of their personal data."

How does it work?

According to Forbes, PEC is a method of collaborating without exposing personal or sensitive data by allowing multiple parties to extract value and achieve meaningful outcomes from data without ever sharing it with those parties.

While PET (Privacy Enhancing Techniques) is an umbrella word, numerous technologies safeguard data.

At the same time, it is being utilised and preserving and augmenting security and privacy while searches or analyses are being done. The following are a few examples of how this technology works in detail:

How Privacy Enhancing Techniques works

Homomorphic encryption

This is the most secure method of encryption since it permits computation in the encrypted or ciphertext area. In this space, it provides two elementary operations.

  • The ability to multiply two homomorphically encrypted data or values together is the first.
  • The second entails adding two homomorphically encrypted values together so that when the product or sum is decrypted, it returns a meaningful result.

Furthermore, there are two varieties of homomorphic encryption

  • completely homomorphic encryption, which provides multiplication in ciphertext space
  • partially homomorphic encryption, which provides multiplication in ciphertext space.

 Both kinds may be used to create algorithms that support fundamental business tasks such as encrypted searches and encrypted analytics such as machine learning and AI.

Zero-knowledge proofs

This cryptographic method enables data verification without disclosing any of the data.

Zero-knowledge proofs are a general notion that states that information may be proven without exposing the underlying facts on which the confirmation is based.

Different varieties of PETs exhibit zero-knowledge proofs.

Multiparty computation

This family approach enables several participants to collaborate on data while keeping their inputs private.

Nothing else than conceivable commercial outcomes can be decoded by a single party.

Privacy Enhancing Computation Techniques

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Personal Data Stores

A personal data store (PDS) provides broad access to individual data and the option for the data owner to upload, distribute, update, or remove this data.

It may contain addresses, phone numbers, passport information, bank account histories, electronic health records, and other information.

This technology allows each individual to govern their own data. A personal data store intends to enable the ability for third-party providers to add or remove private data.

This sort of store provides several advantages for a business, including:

  • More efficient data collection and storage
  • Absence of a law risk to disclose private data without consent
  • Data may be quickly updated

Differential Privacy

Differential privacy is a data analysis and statistics generation technique. The individual data is hidden, and the whole dataset is displayed. When someone enters or quits the dataset, the algorithm practically doesn't change. It ensures that personal information is kept private.

Differential privacy works by adding precisely calculated "noise" to a dataset. For example, if 118 individuals purchased a product after clicking on an advertisement, a differentially private system would add or deduct a random sum from that total. So, instead of 118, a user of such a system would see a number of 120 or 114.

Even if you have a lot of other data, adding that tiny random bit of inaccurate information makes it more difficult to determine who actually bought the goods after clicking the ad.

Trusted execution environments

These are the least effective (and least privacy-preserving) techniques.

TEE security is a perimeter-based security architecture; however, the perimeter is narrow and sits on the hardware chip itself rather than at a network border.

Applications of Privacy Enhancing Computation Techniques

Different types of techniques for privacy-enhancing described above have their differences, but they all share the same goal.

What can you do once you've used datasets to which you don't have access? In many respects, the sky is the limit — this is what has led to the emergence of the provider less trend, in which organisations collaborate directly for various reasons rather than using third-party providers.

Application of Privacy enhancing computation techniques

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Here are a few instances that demonstrate the breadth of application:

HR

If you work for a firm that is serious about gender equality and reducing the gender pay gap, you should join forces with many other companies in your sector or area to monitor the issue using actual data via PEC.

Spoiler alert: This has previously been done by 120 firms in the Boston region. The results were not very positive, but at least they now know what to do about it.

Medical Research

The previous year has demonstrated how critical this is. Many rules safeguard patient records, which is very appropriate.

However, medical research frequently requires the ability to draw from vast volumes of data, sometimes across borders and restrictions, as we have seen.

It is sometimes necessary. This procedure might become both private and painless if PETs are used.

Fraud prevention

Fraudsters usually specialise in a specific industry and target many firms in that industry. If the firms work together directly and use PETs, they will catch the fraudsters swiftly and reliably. Furthermore, if they collaborate to develop a pool of trustworthy identities without revealing personal user data, it will be easy to discover excellent clients.

Internal data analysis

PEC can assist you in performing data analysis without worrying about data sharing between brands or across multiple areas, such as from the United States to the European Union.

PEC allows businesses to interact with competitors or across borders by directly using data, resulting in improved results thanks to more recent data.

Tech executives who wish to protect their companies while still making the most of their data should look into using privacy-enhancing computing to work with rivals or across borders, using data directly.

Companies have more alternatives and better outcomes because you're utilising recent data — not stale data from a third party — and there are no issues about data privacy.

Conclusion

The desire for privacy isn't going away anytime soon. Organisations must be prepared to function in a world that values data security and privacy, whether driven by government law or customer demand.

With a wide range of commercial uses for PETs, an increasing number of enterprises will seek to take advantage of these business-enabling features.

When doing so, keep in mind that not all PETs are made equal. The first step is to define the privacy-related business concern that needs to be solved and then choose the PET that is most suited to handle it.

As regulators, policymakers, and corporations investigate this area, it is critical to understand the variety of approaches and systems that comprise PETs and their various strengths and purposes.

Author
Hamna Imran
Cyber Security student and keen learner, writing articles for several other websites.

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