#homomorphicencryption

[guillaumelauzier]

Data Privacy: Homomorphic Encryption

In today's world, data privacy is becoming increasingly important as more and more sensitive information is being shared and stored online. At the same time, there is a growing need for computations to be performed on this data to gain insights and drive decision-making. Homomorphic encryption is a technique that can help bridge this gap by allowing computations to be performed on encrypted data, without the need for decryption. Homomorphic encryption provides a way to perform calculations on sensitive data while still maintaining its privacy. This is achieved by applying a mathematical transformation to the original data, which results in encrypted data that can be manipulated without the need for decryption. The results of these computations are also encrypted, ensuring that the original data remains secure throughout the entire process. The potential applications of homomorphic encryption are vast, ranging from healthcare to finance to government. For example, healthcare providers could use homomorphic encryption to perform calculations on sensitive patient data without compromising privacy. Financial institutions could use it to perform complex financial analysis on encrypted customer data. And governments could use it to analyze sensitive data for national security purposes. Despite its potential benefits, homomorphic encryption is still a relatively new and emerging technology. Its implementation is still a challenge, as it requires significant computing resources and specialized knowledge. Nonetheless, it has the potential to be a game-changer in the field of data privacy, providing a way to perform computations on sensitive data while keeping that data secure.

FHE & SHE

Fully Homomorphic Encryption (FHE) is the most advanced form of homomorphic encryption. It enables arbitrary computations to be performed on encrypted data, including addition and multiplication of encrypted values. This means that complex calculations can be performed on encrypted data without the need for decryption, ensuring the privacy of the original data is maintained. While FHE is still relatively new and computationally intensive, it has the potential to revolutionize the field of data privacy. Somewhat Homomorphic Encryption (SHE) is another form of homomorphic encryption that allows for limited computations to be performed on encrypted data. SHE is more practical and computationally efficient than FHE, making it a more attractive option for real-world applications. SHE is primarily used in scenarios where only basic computations, such as addition or multiplication, are required on the encrypted data. There are several use cases for homomorphic encryption, including: - Healthcare: Homomorphic encryption can be used in the healthcare sector to enable computations on sensitive patient data while maintaining data privacy. Medical researchers and providers can use homomorphic encryption to perform analyses on encrypted data, including the detection of rare diseases, identifying correlations between different health factors, and discovering new treatments. - Finance: Homomorphic encryption can be used in the finance sector to perform complex financial analysis on encrypted customer data. Banks and other financial institutions can use homomorphic encryption to perform encrypted computations on customer data, such as credit scores, loan eligibility, and fraud detection. - Government: Homomorphic encryption can be used in the government sector for national security purposes. Intelligence agencies can use homomorphic encryption to analyze encrypted data to detect potential threats to national security, while maintaining the privacy of sensitive information. One of the strengths of homomorphic encryption is that it provides a way to maintain data privacy even in the face of quantum computing threats. While traditional encryption methods can be broken by quantum computers, homomorphic encryption remains secure because it encrypts the data at a much higher level of abstraction, making it resistant to brute-force attacks. Homomorphic encryption also enables computations to be performed on encrypted data in a quantum-safe way, providing a secure way to process sensitive information.

Conclusion

Homomorphic encryption is an exciting technology that provides a way to perform computations on sensitive data without compromising its privacy. It has numerous applications in healthcare, finance, government, and many other sectors. While still relatively new, advancements in the field are rapidly increasing its practicality and potential. Homomorphic encryption's ability to maintain data privacy in the face of quantum computing threats is a significant advantage, making it an essential tool for safeguarding sensitive data. As we continue to navigate the complexities of an increasingly digitized world, the demand for privacy and data security will only continue to grow. Homomorphic encryption provides a solution that allows us to analyze and process sensitive data without sacrificing its privacy. Its potential to revolutionize data privacy and security is enormous, and it is essential that we continue to invest in its development and application. As we move forward, we can be confident that homomorphic encryption will play a crucial role in ensuring the security and privacy of our data in the face of an ever-changing technological landscape. Read the full article

[srikalyan]

Homomorphic Encryption

Introduction

Homomorphic encryption allows mathematical operations to be performed on data in its encrypted form, without the need to decrypt the data. This is possible because the encryption scheme preserves the structure of the data, allowing mathematical operations to be performed on the ciphertext in a way that is consistent with the operations performed on the plaintext. For example, if two numbers are added together in plaintext, the same operation can be performed on their encrypted representations, resulting in an encrypted version of the sum. This property allows for the secure computation of functions on encrypted data, without revealing the underlying data to the party performing the computation.

There are three major types of homomorphic encryption

Partially Homomorphic Encryption (PHE): Allows only one operation to be performed on the encrypted data and for an infinite number of times

Somewhat Homomorphic Encryption (SHE): Allows both additions and multiplications to be performed on the encrypted data but only for a finite number of times

Fully homomorphic encryption (FHE): Allows additions, multiplications, and other arbitrary mathematical operations to be performed on the encrypted data and for an infinite number of times

As the computations are being performed on encrypted data, homomorphic encryption algorithms are usually super compute-intensive and come with a significant performance overhead. Thanks to advancements in software such as batching/packaging and in hardware such as SIMD, GPU, FPGA, and ASIC, the cost of performance is getting reduced by a factor of 10 every year (see the image below).

Schemes

In the context of encryption, a scheme typically refers to a specific method or algorithm for encrypting and decrypting data. The 3 major popular schemes in homomorphic encryption are

TFHE: Allows operations on single values at the bit level

BGV/BGF: Allows exact arithmetic on vectors of numbers (fixed point)

CKKS: Allows approximate arithmetic on vectors of numbers (floating point, real numbers)

A few homomorphic encryption implementations are openfhe, SEAL, Palisade, Lattigo, Concrete, etc

Usecases

Homomorphic encryption is a relatively new technology, and as such, it is not yet widely used. However, there are a growing number of organizations and individuals who are exploring its potential uses. Some examples of potential applications of homomorphic encryption include:

Secure cloud computing: Homomorphic encryption could be used to allow sensitive data to be processed in the cloud without revealing it to the cloud provider.

Secure multiparty computation: Homomorphic encryption could be used to enable multiple parties to jointly compute a function on their encrypted data, without revealing their data to each other.

Privacy-preserving data analysis: Homomorphic encryption could be used to allow data to be analyzed without revealing the underlying data to the party performing the analysis.

Private Information Retrieval: Homomorphic encryption could be used to execute queries privately on public/private databases.

Better Model Generation: Homomorphic encryption in conjunction with secret sharing could be leveraged to vertically combine data from multiple sources for the generation of better models.

Overall, while homomorphic encryption is not yet widely used, it has the potential to enable a wide range of applications in areas where security and privacy are of critical importance.

Companies

Companies that are trying to commercialize homomorphic encryption are

Duality, a startup leveraging homomorphic encryption to provide services that help clients share data and perform computations without compromising privacy. They have utilized homomorphic encryption and secure multiparty computation to perform large-scale genome-wide association studies in a secure way beating the state-of-the-art system by at least one order of magnitude. Google fully integrated their fully homomorphic encryption transpiler with duality’s open-source library to enable application developers to leverage the technology with minimal knowledge.

EnVeil, a startup that is trying to develop tools to support higher-order operations on top of additions and multiplication operations

There are many smaller startups that are trying to leverage the technology e.g., Zama.ai, ShieldIO, etc

[covidimpact]

Homomorphic Encryption Market | Global Industry Trends, Segmentation, Business Opportunities & Forecast To 2026

The newly added Homomorphic Encryption Market research report by Value Market Research disclose all the important information associated with the market such as value, growth factor, trends, market share, size, and challenges for the forecasted timeline 2019-2026. Further, this report also highlights smart strategy adopted by major players and also their market share. Basically, this report is designed to give a proper understanding of industry structure and competition intensity attractiveness.

The research report also covers the comprehensive profiles of the key players in the market and an in-depth view of the competitive landscape worldwide. The major players in the homomorphic encryption market include Cosmian, CryptoExperts, Gemalto, Google LLC, IBM Corporation, Inpher, Microsoft, Corporation, Netskope, ShieldIO, Zama. This section includes a holistic view of the competitive landscape that includes various strategic developments such as key mergers & acquisitions, future capacities, partnerships, financial overviews, collaborations, new product developments, new product launches, and other developments.  

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 Market Dynamics

The market growth is mainly influenced by the rising usage of smart-devices like smartphones, tablets, and mobility solutions, rising demand for secure and safe data transmission, accelerating investment in cloud-based industries, and increasing e-governance initiatives. In addition to this, the rising implementation of homomorphic encryption in the BFSI industry is likely to boost demand over the forecast period. However, high setup cost and complexity of systems are the factors impede the growth of the market. The report covers Porter’s Five Forces Model, Market Attractiveness Analysis and Value Chain analysis. These tools help to get a clear picture of the industry’s structure and evaluate the competition attractiveness at a global level. Additionally, these tools also give inclusive assessment of each segment in the global market of homomorphic encryption.

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Market Segmentation

The report segments the homomorphic encryption market and analyses it with respect to the geography, intending to keep the marketer informed and help them identify the target demographics for the product or service. By Type

Fully

Additive

Partial

Multiplicative

By Application

BFSI

Healthcare

Government

Others

Regional Analysis

This section covers regional segmentation which accentuates on current and future demand for aerosol valve market across North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa. Further, the report focuses on demand for individual application segment across all the prominent regions.

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[dimitryp]

Distributed SGD as an alternative to homomorphic encryption

The purpose of homomorphic encryption is to allow computation on encrypted data. Computation is deterministic and serves some decision-making process.

The purpose of probabilistic model (deep or shallow) is to suggest the best output given input. Probabilistic model always serves some decision-making process too.

An important distinction between computation and probability distribution is that probability distribution compresses information, but not all of it - just the information relevant to the target decision-making process- and discards the rest.

This feature of probabilistic models can be used to share the decision-making process without sharing data. This is analogous to a human-consultant: she knows private information of every client and can't transfer it across clients, but she can transfer her expertise acquired from knowing the private information of clients. Probabilistic models are even better than a human consultant: their capacity can be controlled to fine-tune how much information content of the training-data can be recovered from a trained model.

The most straightforward implementation of such system can look like a distributed mini-batch gradient descent: each party uses its private data to form mini-batches, and they only transfer the parameters of the model between each other - in a cross-organizational distributed SGD process. Neither party is incentivized to spoil such distributed training process with fake data because they won't be able to "recover" unspoiled parameters after other parties perform parameters-updates using their batches.

This last property is somewhat analogous to a block-chain concept: it is impossible to "rewrite" a parameters-update step in the middle of the process while keeping the subsequent steps unchanged.