The importance of data for enterprises has increased profoundly in recent times. It helps businesses make decisions quickly with better possibilities for success. On the other hand, enterprises also have to deal with a continuously expanding wave of data risk challenges. So, many data protection regulations such as CCPA and GDPR are encouraging organizations to safeguard consumer data.
At the same time, matters get more complicated with the growing adoption of technologies that allow individuals to connect and communicate with each other. The increasing dominance of such issues fostered the growth of top privacy-enhancing technologies. As the volume of personal information of users on the network continues to increase every day, privacy is definitely an essential requirement. Let us find out more about privacy enhancing technologies (PETs) and some of the top PETs in 2021.
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Why Do You Need PETs?
Various recent developments have been responsible for increasing awareness regarding the need for privacy and security in online activities. The technologies that help people connect and communicate with each other, such as online social networks, instant messaging apps, email, and others, leave a large volume of personal information available online.
Subsequently, corporations have shown additional interest in collecting the personal information generated by individuals. Corporations use the sensitive personal information of users to perform various tasks such as personalization, auto insurance rates reduction, targeted advertising, and more.
At the same time, government surveillance on individual communications alongside other online activities also creates questions on privacy. Furthermore, the best privacy enhancing technologies are clearly essential for countering the concerns of data breaches at government institutions and private corporations.
All of these factors showcase the possibilities of risks in linking data traffic and identity, disclosing location for data transfer, information disclosure, and identity disclosure. Privacy enhancing technologies or PETs can serve as plausible alternatives for solving these problems. However, it is also important to know the definition of PETs before diving into an outline of the top alternatives.
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Definition of Privacy Enhancing Technologies (PETs)
When you are looking for the top privacy enhancing technologies, it is reasonable to find their definition. However, you may end up disappointed if you look for a single definition for PETs. Privacy enhancing technologies commonly point out approaches or technologies which could help in resolving security and privacy risks. On the other hand, many leading researchers have attempted to provide standard definitions for PETs.
One of the widely accepted definitions of privacy enhancing technologies suggests that they are a wide assortment of technical instruments and approaches tailored for safeguarding the privacy of users. Similarly, PETs also have a unique definition from the perspective of industry stakeholders. Industry stakeholders assume PETs as the different technical ways for ensuring privacy protection through the facility of anonymity, opaqueness, inaccessibility, pseudonymous identity for data subjects.
Policymakers have different connotations for PETs as they consider them as technical tools or methods for achieving compliance with data protection requirements or legislations. Generally, policymakers imply the functionality of PETs in unison with different organizational measures. The organizational measures include personnel management and access controls, audits, information security policies, and procedures.
The most commonly accepted definition for understanding the best privacy enhancing technologies comes from ENISA or The European Union Agency for Cybersecurity. ENISA classified privacy enhancing technologies as the special type of technology tailored for supporting pseudonymous identity for data, anonymity or data and minimizing data. The definition of ENISA also suggests that PETs have been tailored for supporting core data protection and privacy principles.
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Popularity of PETs
The principles associated with top privacy enhancing technologies are being largely integrated into laws for entities involved in processing personal data. With the help of such laws, entities would have to use the best practices for safeguarding and maintaining personal data. One of the prominent examples of such obligations can be identified in the case of the General Data Protection Regulation (GDPR) of Europe. GDPR states that data controllers should deploy data protection as a default requirement and basic design aspect. In addition, GDPR also posits that data controllers should also use state-of-the-art technological processes for employing data protection.
Now, it is quite confusing to determine the state-of-the-art technological processes for data protection. As a matter of fact, the evolution of technological processes points out the need for constant evaluation of tools. The same is applicable for the best privacy enhancing technologies that continue to set new benchmarks in data privacy.
The popularity of privacy enhancing technologies is largely the artwork of the GDPR with the contributions of the California Consumer Privacy Act (CCPA) and the successive California Privacy Rights Act (CPRA) as well as other new data protection and privacy protection regulations and laws worldwide. As of now, PETs represent a significantly growing market with a profound influx of investment.
On the other hand, PETs are yet to get a proper definition before they move out of the generalized classification under ‘privacy tech’ or digital rights management techniques. Therefore, it is extremely difficult to place an accurate value for the privacy enhancing technology solutions market. Interestingly, the European market for homomorphic encryption tools had reached the value of $31.99 million in 2019. Estimates suggest that the privacy enhancing technology of homomorphic encryption tools would achieve a market value of almost $66.50 million by 2027.
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Top Picks among Privacy Enhancing Technologies (PETs)
Enterprises are looking at every possibility of innovation to address the concerns of data security and privacy. Most important of all, businesses are not only focusing on safeguarding data privacy in direct interactions with customers but also in B2B communications. As a result, many enterprises are interested in finding out the top privacy enhancing technologies. Here is an outline of some of the common entries among popular privacy enhancing technologies.
1. Homomorphic Encryption
Considered the most secure option, homomorphic encryption is often referred to as the ‘holy grail’ of encryption. The most interesting feature of homomorphic encryption is the support for computation in ciphertext or encrypted form. Furthermore, you should also note that homomorphic encryption is not a new technology and has been around in academic discussion for over 30 years.
The conventional perspectives on homomorphic encryption focused largely on its computationally intensive nature. However, recent developments have made homomorphic encryption one of the best privacy enhancing technologies around. Homomorphic encryption enables two primary operations in the encrypted or ciphertext domain.
The first operation refers to the ability for the multiplication of two different homomorphically encrypted values. The second operation involves the addition of two homomorphically encrypted values. Homomorphic encryption ensures that decryption of the product or sum could offer a meaningful value.
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2. Differential Privacy
Another entry among top privacy enhancing technologies in 2021 is differential privacy. It is actually a rigorous mathematical representation of privacy with quantification of risk due to the inclusion of an individual in a data set. Differential privacy utilizes techniques for anonymity with the addition of statistical ‘noise’ to data sets before computation of calculation and results. It is also important to note that differential privacy could be local or global.
Global DP basically involves server-side anonymity of identity or de-identification, while local DP focuses on the application in the client device. Presently, many differentially private variants of machine learning algorithms, statistical estimates, streaming and game theory, and economic mechanism design. Differential privacy is better suited for larger databases due to the diminishing effect of a particular individual on a particular aggregate statistic alongside growth in the number of individuals in the database.
3. Generative Adversarial Networks
The scope of innovation is also one of the prolific reasons for coming across the best privacy enhancing technologies like GANs. Generative Adversarial Networks or GANs are actually a variant of artificial intelligence focused on creating algorithms in pairs. One of the algorithms focuses on learning while the other entry in the pair works as the judge.
Generative Adversarial Networks find prominent applications in unsupervised machine learning. Their application involves competition between two neural networks in a framework for delivering profoundly effective simulation of real data. The most prolific application of GANs is evident in the development of synthetic data sets.
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4. Secure Multi-Party Computation
If you look further into top privacy enhancing technologies, you are most likely to come across secure multi-party computation (SMPC). Secure multi-party computation is actually a distributed computing system or technology capable of offering abilities for computing values of interest. SMPC takes input from multiple encrypted data sources without any party revealing private data to others.
The most common example of secure multi-party computation is evident in secret sharing. In the process of secret sharing, data from each party has to be divided and distributed in the form of random, encrypted shares between the parties. The combination of random shares could help you in obtaining the desired statistical result.
5. Identity Management
Identity management solutions utilize different platforms such as distributed ledger technology and local processing to help individuals validate their identity. Identity management solutions are also capable of capitalizing on the device-level machine learning capabilities for verification and validation.
Therefore, people without any internet access develop secure connections and exchange identity-based credentials without the involvement of centralized intermediaries. The users’ device could help access the verified personal data and share it through secure channels to third parties.
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6. Zero Knowledge Proofs
Zero Knowledge Proofs or ZKPs are also one of the examples of best privacy enhancing technologies in the present times. ZKPs are actually cryptographic methods that allow one party to prove that they know a fact to another party. Interestingly, the first party does not have to disclose any additional information about the fact to the second party.
The applications of ZKPs could make a profound mark in identity verification contexts. For example, ZKPs could help in proving the age of an individual without revealing their personal information such as date of birth. Basically, zero knowledge proofs support data minimization and safeguarding. In addition, ZKPs also ensure the incorporation of privacy as a default element in transaction design.
Also Read: How Can ZKP Deliver Better Security?
7. Synthetic Data Sets
Synthetic data sets also represent another prolific addition to top privacy enhancing technologies. They are basically collections of artificial data developed for replicating the patterns and analytical potential of real data about individuals or events. Synthetic data sets are created through the replication of significant statistical traits in the real data.
Interestingly, it is possible to create synthetic data sets at a massive scale while reducing the necessity of large test data or training sets. They find promising applications in AI and ML use cases with a focus on reducing data sharing or secondary use concerns.
8. Federated Learning
Federated learning is unique to other PETs due to support for enabling the training of automated learning models from data. Interestingly, the data never leaves the company or the device in which it was generated. The application of federated learning as a privacy enhancing technology would find prolific value in IoT use cases. For example, federated learning could help in training intelligence systems in virtual assistants without compromising data integrity.
9. Edge Computing and Local Processing
The use of edge computing and local processing in combination is also one of the best privacy enhancing technologies. It involves running away of applications, data, and services from centralized nodes at the network’s endpoints. This is an evident highlight in cases of devices that need high speed or without any constant connectivity. Local processing addresses the need for data minimization through the reduction of the amount of data that the service provider should collect or retain in cloud storage or centralized service.
10. Trusted Execution Environments
Trusted Execution Environments or TEEs are also one of the promising examples of PETs. However, they are known for being the least secure option among the top privacy enhancing technologies. The security for TEEs basically involves a perimeter-based security model. With all the information decrypted in the perimeter of the on-chain assembly, TEEs can ensure faster computational abilities. So, TEEs are suitable in use cases that don’t have strict security and privacy restrictions.
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It is clearly evident that privacy is not just a new technology trend everyone is running after. At the same time, the importance of privacy enhancing technologies also becomes quite relevant in the present times. Increasing concerns of data breaches, new vulnerabilities, and changing regulatory landscapes for data protection and privacy drive the need for PETs.
Privacy enhancing technologies provide the most reliable answers for enterprises having data security and privacy as their priorities. Gradually, many businesses are leveraging the capabilities of best privacy enhancing technologies to derive value for their operations. However, it is also important to remember that PETs are considerably different from each other and fit with certain applications. Find out more about PETs and how to position them perfectly for desired advantages right now!
The significance of information for enterprises has elevated profoundly in latest instances. It helps companies make choices rapidly with higher potentialities for achievement. However, enterprises additionally should cope with a repeatedly increasing wave of information danger challenges. So, many knowledge safety rules reminiscent of CCPA and GDPR are encouraging organizations to safeguard shopper knowledge.
On the identical time, issues get extra difficult with the rising adoption of applied sciences that permit people to attach and talk with one another. The growing dominance of such points fostered the expansion of high privacy-enhancing applied sciences. As the quantity of private info of customers on the community continues to extend each day, privateness is unquestionably a necessary requirement. Allow us to discover out extra about privateness enhancing applied sciences (PETs) and a number of the high PETs in 2021.
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Why Do You Want PETs?
Varied latest developments have been accountable for growing consciousness concerning the necessity for privateness and safety in on-line actions. The applied sciences that assist individuals join and talk with one another, reminiscent of on-line social networks, instantaneous messaging apps, electronic mail, and others, depart a big quantity of private info out there on-line.
Subsequently, companies have proven extra curiosity in gathering the non-public info generated by people. Companies use the delicate private info of customers to carry out varied duties reminiscent of personalization, auto insurance coverage charges discount, focused promoting, and extra.
On the identical time, authorities surveillance on particular person communications alongside different on-line actions additionally creates questions on privateness. Moreover, the greatest privateness enhancing applied sciences are clearly important for countering the considerations of information breaches at authorities establishments and personal companies.
All of those components showcase the probabilities of dangers in linking knowledge visitors and identification, disclosing location for knowledge switch, info disclosure, and identification disclosure. Privateness enhancing applied sciences or PETs can function believable options for fixing these issues. Nevertheless, it is usually vital to know the definition of PETs earlier than diving into a top level view of the highest options.
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Definition of Privateness Enhancing Applied sciences (PETs)
If you find yourself searching for the high privateness enhancing applied sciences, it’s affordable to search out their definition. Nevertheless, you might find yourself dissatisfied when you search for a single definition for PETs. Privateness enhancing applied sciences generally level out approaches or applied sciences which may assist in resolving safety and privateness dangers. However, many main researchers have tried to supply commonplace definitions for PETs.
One of many extensively accepted definitions of privateness enhancing applied sciences means that they’re a large assortment of technical devices and approaches tailor-made for safeguarding the privateness of customers. Equally, PETs even have a novel definition from the attitude of trade stakeholders. Business stakeholders assume PETs because the totally different technical methods for making certain privateness safety by means of the ability of anonymity, opaqueness, inaccessibility, pseudonymous identification for knowledge topics.
Policymakers have totally different connotations for PETs as they think about them as technical instruments or strategies for attaining compliance with knowledge safety necessities or legislations. Usually, policymakers indicate the performance of PETs in unison with totally different organizational measures. The organizational measures embrace personnel administration and entry controls, audits, info safety insurance policies, and procedures.
Essentially the most generally accepted definition for understanding the greatest privateness enhancing applied sciences comes from ENISA or The European Union Company for Cybersecurity. ENISA categorised privateness enhancing applied sciences because the particular kind of know-how tailor-made for supporting pseudonymous identification for knowledge, anonymity or knowledge and minimizing knowledge. The definition of ENISA additionally means that PETs have been tailor-made for supporting core knowledge safety and privateness ideas.
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Recognition of PETs
The ideas related to high privateness enhancing applied sciences are being largely built-in into legal guidelines for entities concerned in processing private knowledge. With the assistance of such legal guidelines, entities must use one of the best practices for safeguarding and sustaining private knowledge. One of many outstanding examples of such obligations may be recognized within the case of the Normal Knowledge Safety Regulation (GDPR) of Europe. GDPR states that knowledge controllers ought to deploy knowledge safety as a default requirement and fundamental design facet. As well as, GDPR additionally posits that knowledge controllers also needs to use state-of-the-art technological processes for using knowledge safety.
Now, it’s fairly complicated to find out the state-of-the-art technological processes for knowledge safety. As a matter of truth, the evolution of technological processes factors out the necessity for fixed analysis of instruments. The identical is relevant for the greatest privateness enhancing applied sciences that proceed to set new benchmarks in knowledge privateness.
The recognition of privateness enhancing applied sciences is essentially the paintings of the GDPR with the contributions of the California Shopper Privateness Act (CCPA) and the successive California Privateness Rights Act (CPRA) in addition to different new knowledge safety and privateness safety rules and legal guidelines worldwide. As of now, PETs signify a considerably rising market with a profound inflow of funding.
However, PETs are but to get a correct definition earlier than they transfer out of the generalized classification beneath ‘privateness tech’ or digital rights administration strategies. Subsequently, this can be very tough to put an correct worth for the privateness enhancing know-how options market. Curiously, the European marketplace for homomorphic encryption instruments had reached the worth of $31.99 million in 2019. Estimates recommend that the privateness enhancing know-how of homomorphic encryption instruments would obtain a market worth of just about $66.50 million by 2027.
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High Picks amongst Privateness Enhancing Applied sciences (PETs)
Enterprises are taking a look at each risk of innovation to deal with the considerations of information safety and privateness. Most vital of all, companies usually are not solely specializing in safeguarding knowledge privateness in direct interactions with prospects but additionally in B2B communications. Because of this, many enterprises are excited by discovering out the high privateness enhancing applied sciences. Right here is a top level view of a number of the frequent entries amongst standard privateness enhancing applied sciences.
1. Homomorphic Encryption
Thought-about probably the most safe possibility, homomorphic encryption is also known as the ‘holy grail’ of encryption. Essentially the most attention-grabbing function of homomorphic encryption is the assist for computation in ciphertext or encrypted kind. Moreover, you also needs to word that homomorphic encryption just isn’t a brand new know-how and has been round in educational dialogue for over 30 years.
The traditional views on homomorphic encryption centered largely on its computationally intensive nature. Nevertheless, latest developments have made homomorphic encryption one of many greatest privateness enhancing applied sciences round. Homomorphic encryption allows two major operations within the encrypted or ciphertext area.
The primary operation refers back to the skill for the multiplication of two totally different homomorphically encrypted values. The second operation entails the addition of two homomorphically encrypted values. Homomorphic encryption ensures that decryption of the product or sum may provide a significant worth.
Right here’s a information to know How Does Blockchain Use Public Key Cryptography?
2. Differential Privateness
One other entry amongst high privateness enhancing applied sciences in 2021 is differential privateness. It’s really a rigorous mathematical illustration of privateness with quantification of danger because of the inclusion of a person in an information set. Differential privateness makes use of strategies for anonymity with the addition of statistical ‘noise’ to knowledge units earlier than computation of calculation and outcomes. It is usually vital to notice that differential privateness could possibly be native or international.
World DP principally entails server-side anonymity of identification or de-identification, whereas native DP focuses on the applying within the shopper gadget. Presently, many differentially personal variants of machine studying algorithms, statistical estimates, streaming and sport concept, and financial mechanism design. Differential privateness is best suited to bigger databases because of the diminishing impact of a selected particular person on a selected combination statistic alongside development within the variety of people within the database.
3. Generative Adversarial Networks
The scope of innovation can be one of many prolific causes for coming throughout the greatest privateness enhancing applied sciences like GANs. Generative Adversarial Networks or GANs are literally a variant of synthetic intelligence centered on creating algorithms in pairs. One of many algorithms focuses on studying whereas the opposite entry within the pair works because the decide.
Generative Adversarial Networks discover outstanding functions in unsupervised machine studying. Their software entails competitors between two neural networks in a framework for delivering profoundly efficient simulation of actual knowledge. Essentially the most prolific software of GANs is clear within the growth of artificial knowledge units.
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4. Safe Multi-Get together Computation
When you look additional into high privateness enhancing applied sciences, you’re almost certainly to return throughout safe multi-party computation (SMPC). Safe multi-party computation is definitely a distributed computing system or know-how able to providing talents for computing values of curiosity. SMPC takes enter from a number of encrypted knowledge sources with none social gathering revealing personal knowledge to others.
The commonest instance of safe multi-party computation is clear in secret sharing. Within the technique of secret sharing, knowledge from every social gathering needs to be divided and distributed within the type of random, encrypted shares between the events. The mixture of random shares may enable you in acquiring the specified statistical outcome.
5. Id Administration
Id administration options make the most of totally different platforms reminiscent of distributed ledger know-how and native processing to assist people validate their identification. Id administration options are additionally able to capitalizing on the device-level machine studying capabilities for verification and validation.
Subsequently, individuals with none web entry develop safe connections and alternate identity-based credentials with out the involvement of centralized intermediaries. The customers’ gadget may assist entry the verified private knowledge and share it by means of safe channels to 3rd events.
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6. Zero Data Proofs
Zero Data Proofs or ZKPs are additionally one of many examples of greatest privateness enhancing applied sciences within the current instances. ZKPs are literally cryptographic strategies that permit one social gathering to show that they know a truth to a different social gathering. Curiously, the primary social gathering doesn’t should disclose any extra details about the very fact to the second social gathering.
The functions of ZKPs may make a profound mark in identification verification contexts. For instance, ZKPs may assist in proving the age of a person with out revealing their private info reminiscent of date of start. Mainly, zero information proofs assist knowledge minimization and safeguarding. As well as, ZKPs additionally make sure the incorporation of privateness as a default component in transaction design.
Additionally Learn: How Can ZKP Ship Higher Safety?
7. Artificial Knowledge Units
Artificial knowledge units additionally signify one other prolific addition to high privateness enhancing applied sciences. They’re principally collections of synthetic knowledge developed for replicating the patterns and analytical potential of actual knowledge about people or occasions. Artificial knowledge units are created by means of the replication of serious statistical traits in the true knowledge.
Curiously, it’s attainable to create artificial knowledge units at a large scale whereas lowering the need of huge take a look at knowledge or coaching units. They discover promising functions in AI and ML use circumstances with a give attention to lowering knowledge sharing or secondary use considerations.
8. Federated Studying
Federated studying is exclusive to different PETs on account of assist for enabling the coaching of automated studying fashions from knowledge. Curiously, the information by no means leaves the corporate or the gadget through which it was generated. The applying of federated studying as a privateness enhancing know-how would discover prolific worth in IoT use circumstances. For instance, federated studying may assist in coaching intelligence programs in digital assistants with out compromising knowledge integrity.
9. Edge Computing and Native Processing
The usage of edge computing and native processing together can be one of many greatest privateness enhancing applied sciences. It entails working away of functions, knowledge, and companies from centralized nodes on the community’s endpoints. That is an evident spotlight in circumstances of gadgets that want excessive velocity or with none fixed connectivity. Native processing addresses the necessity for knowledge minimization by means of the discount of the quantity of information that the service supplier ought to accumulate or retain in cloud storage or centralized service.
10. Trusted Execution Environments
Trusted Execution Environments or TEEs are additionally one of many promising examples of PETs. Nevertheless, they’re identified for being the least safe possibility among the many high privateness enhancing applied sciences. The safety for TEEs principally entails a perimeter-based safety mannequin. With all the knowledge decrypted within the perimeter of the on-chain meeting, TEEs can guarantee quicker computational talents. So, TEEs are appropriate in use circumstances that don’t have strict safety and privateness restrictions.
Watch now the total on-demand webinar on Approaches to Overcome Scalability, Confidentiality, and Knowledge Privateness Points for Enterprise Blockchains
It’s clearly evident that privateness is not only a brand new know-how development everyone seems to be working after. On the identical time, the significance of privateness enhancing applied sciences additionally turns into fairly related within the current instances. Rising considerations of information breaches, new vulnerabilities, and altering regulatory landscapes for knowledge safety and privateness drive the necessity for PETs.
Privateness enhancing applied sciences present probably the most dependable solutions for enterprises having knowledge safety and privateness as their priorities. Progressively, many companies are leveraging the capabilities of greatest privateness enhancing applied sciences to derive worth for his or her operations. Nevertheless, it is usually vital to keep in mind that PETs are significantly totally different from one another and match with sure functions. Discover out extra about PETs and easy methods to place them completely for desired benefits proper now!