Every organization collects the data of the consumers on a large scale, they are obliged to protect them in accordance with the existing privacy laws and regulations to avoid fines. To protect those data different measures are taken like anonymization and encryption. But as data are collected on a large scale and as organizations are facing lots of data breach incidents, the need for new privacy-enhancing technologies is something that needs to be looked out for, which helps in protecting and securing the collected data.
What is Privacy Enhancing Technologies (PET)?
Privacy-enhancing technologies or PET are designed to prevent data leaks when balancing privacy with usability. Some PETs even prevent the bad actors from identifying to whom the collected data belongs. If the data leak is about to happen, then the data would be useless to criminals as the data will not be easily identifiable.
Mainly PETs are designed for preventing disastrous data leaks. These technologies are important in sectors that rely on the extensive collection and use of sensitive data such as financial services and healthcare. The main goal of Privacy Enhancing Technology is to protect data from unauthorized access, processing, or distribution.
Types of Pets
Privacy-enhancing technology covers various methods that help in protecting the privacy and confidentiality of the data. Different types of PETs are used to protect sensitive data which include,
- PETs focusing on altering the data
- Group of PETs focusing on hiding or shielding data
- Another Broad category of PETs that represent new systems and data architectures for processing, managing, and storing data
Methods like anonymization and pseudonymization help in altering the data. Methods like encryption and homomorphic encryption help in hiding or shielding the data. These different methods can be adopted to safeguard the data.
Homomorphic encryption is one of the privacy-enhancing technologies that allow computations on encrypted data and deliver encrypted results. It is a cryptographic technique that allows mathematical operations to be performed on encrypted data, without the need to decrypt it. This allows the sensitive data to be processed and analyzed without exposing it to the risk of being accessed or stolen.
Goals of homomorphic encryption
Protecting the confidentiality of the stored transmitted data is the goal of conventional modern encryption. The main thing about good encryption is that even if someone accesses encrypted data it cannot be understood easily. The general principle of the encrypted data should be indistinguishable from the random data. As a result, perfectly encrypted data leaks no information.
The goal of homomorphic encryption differs from general encryption. It should be possible to compute with and extract information from encrypted data without access to the key for the encrypted data. Homomorphic encryption is less-than-ideal encryption because the data does not completely prevent information leakage.
Comparing traditional encryption and homomorphic encryption, the more secure method is said to be homomorphic encryption.
Types of homomorphic encryption
Different homomorphic encryption methods are used to secure the data. There are three main types of homomorphic encryption, that includes the following,
- Partially Homomorphic Encryption (PHE) – This keeps the sensitive data secure by only allowing selected mathematical functions to be performed on encrypted data
- Somewhat Homomorphic Encryption (SHE)– Thia supports only limited operations that can be performed only a set number of times
- Fully Homomorphic Encryption (FHE) – This is considered to be the gold standard of homomorphic encryption that keeps information secure and accessible at all times.
Fully Homomorphic Encryption is a technology that has far-reaching implications for secure data analysis. Its applications are of high interest in financial services, health care, government, and wider industry stakeholders.
The International Organization for Standardization and the International Electrotechnical Commission initiated an official project to establish FHE standards. Currently, the project is in the comments resolution phase with the passing of the first-of-its-kind FHE standardization expected within the next few years. Potential users and stakeholders who can benefit from being informed and staying up to date about FHE applications include developers, customers, and regulators.
The other standardization bodies, such as the National Institute of Standards and Technology, are supporting developments for the successful adoption and recognition of FHE.
Considering FHE in the domain of data privacy, it holds great potential to revolutionize data privacy and data security across industries. The increasing international developments and political support for PETs show they are no longer confined to academic research and will soon enter the mainstream.
Practical applications of homomorphic encryption
There are some practical applications for homomorphic encryption in the real world, which include the following,
- Securely storing and computing sensitive or regulated data in Cloud
- Electronic Voting Systems
- Overcoming Data Regulatory requirements like GDPR
- Making financial institutions secure and fraud-proof
- Making the Internet more secure
Advantages and disadvantages of homomorphic encryption
The following are the advantages and disadvantages of homomorphic encryption.
The advantages include,
- A third party can perform operations on encrypted data which is revolutionary and can radically transform the internet
- Homomorphic encryption ensures data privacy since the information or the data is not shared in plain text. In case a hacker steals this information, he will be looking at a pile of encrypted data which cannot be encrypted.
The disadvantages include,
- This method is computationally expensive, as it introduces significant overheads compared to performing the operations on non-encrypted data.
- Fully Homomorphic encryption seems to be slow, and SHE and PHE are only suitable for specific applications.
Homomorphic encryption and data privacy
Generally, Privacy Enhancing Technologies respect fundamental data protection principles such as lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity, confidentiality, and accountability.
Regarding the GDPR, there are arguments for homomorphic encryption being a technique of pseudonymization as well as anonymization. It is expected that more tech companies will begin looking into homomorphic encryption and alternatives to it, working to speed up the data storage and management process as a whole. This will be a critical step forward for the entire IT industry and organizational interest in methods like homomorphic encryption will likely stretch far beyond GDPR compliance when these methods begin showing improvements in not just data privacy, but also speedy increases for general data management for a larger scale
The European Union’s General Data Protection Regulations (GDPR) and data regulators across the world have concluded that homomorphic encryption is presently suitable only for computational privacy.
While homomorphic encryption and other PETs show great promise, challenges persist in their widespread adoption. The absence of widely recognized standards and regulatory certainties can hinder the interoperability and compatibility of PETs, making it difficult for organizations to integrate them into existing systems or making companies hesitant to adopt PETs due to potential compliance issues. Additionally, the technical complexity and performance trade-offs can hinder adoption.
The ongoing ISO standardization efforts for FHE are crucial in addressing these challenges. Establishing standards will not only improve interoperability and compatibility but also provide regulatory recognition, further emphasizing the importance of FHE in data privacy and security.