Homomorphic Encryption & Federated Learning in Data Analysis

Data plays a pivotal role in the success of businesses, yet safeguarding its privacy and adhering to regulatory standards pose significant challenges. Discover privacy-enhancing technologies that safeguard data integrity.

Homomorphic Encryption Explained

Data can exist in one of three states: at rest, in transit, and in use. Most encryption methods primarily address data at rest or in transit. This is because data in these states remains unchanged; its decrypted value remains consistent with its encrypted counterpart.

Conversely, data in use lacks this stability. Nearly all mathematical operations on ciphertexts alter the corresponding plaintext, making it challenging to ensure that the plaintext changes appropriately.

The fundamental objective of homomorphic encryption is to facilitate computations on encrypted data. This unique encryption method ensures data confidentiality during processing, enabling essential tasks to be executed while the data remains secure, even in untrusted environments. In today’s landscape of distributed computation and varied networking, this capability holds immense value.

A homomorphic cryptosystem operates similarly to conventional public encryption systems, employing a public key to encrypt data and granting access to the unencrypted data solely to the holder of the corresponding private key. However, what sets it apart is its utilization of an algebraic system that allows for a wide range of computations or operations on the encrypted data.

In mathematical terms, “homomorphic” denotes the conversion of one dataset into another while preserving the relationships among elements in both sets, deriving from Greek terms indicating “same structure.” Because data in a homomorphic encryption scheme retains this consistent structure, performing identical mathematical operations on encrypted or decrypted data yields identical results.

In practical applications, most homomorphic encryption systems excel when dealing with data represented as integers and when employing operational functions such as addition and multiplication. This enables manipulation and analysis of encrypted data as if it were in plaintext format, without requiring decryption. Encrypted data can undergo computation and processing to yield encrypted outcomes, with only the holder of the private key able to decrypt the ciphertext and comprehend its significance.

Advantages of Homomorphic Encryption

1. Conventional encryption techniques are effective in safeguarding sensitive data within cloud environments.
2. However, the need to examine or validate encrypted data in such settings poses challenges for organizations.
3. Decrypting data carries inherent security risks and can be costly and time-consuming.
4. Homomorphic encryption addresses these challenges by allowing organizations to securely share private data for evaluation without compromising privacy.
5. It enables the execution of mathematical operations on encrypted data while maintaining data confidentiality.
6. Cloud service providers have access solely to encrypted data, allowing them to perform computations without decryption.
7. Encrypted results are furnished to the owner of the private data for decryption using the appropriate private key.
8. This approach ensures data security throughout the computational process, overcoming the limitations of traditional encryption

Homomorphic Encryption categories

Partially Homomorphic Encryption

Partially homomorphic encryption algorithms enable a specific operation to be performed an infinite number of times. For instance, a particular algorithm may be additively homomorphic, meaning that adding two ciphertexts yields the same outcome as encrypting the sum of the two plaintexts.

Partially homomorphic encryption algorithms are relatively straightforward to design. Notably, certain common encryption algorithms exhibit partial homomorphism unintentionally. For example, the RSA algorithm is multiplicatively homomorphic due to its basis in exponentiation.

Somewhat Homomorphic Encryption

Somewhat homomorphic encryption represents a step beyond partial homomorphism. Such algorithms permit a finite number of any operation rather than an infinite number of a specific operation.

For instance, a somewhat homomorphic encryption algorithm may support any combination of up to five additions or multiplications. However, performing a sixth operation of either type would yield an invalid outcome.

Fully Homomorphic Encryption

Fully homomorphic encryption epitomizes the pinnacle of homomorphic encryption. These algorithms enable an infinite number of additions or multiplications of ciphertexts while still generating a valid result.

Although fully homomorphic encryption algorithms exist today— with the first being devised by Craig Gentry in 2009— subsequent developments have refined and enhanced these original algorithms.

Federated Learning

Federated learning revolutionizes machine learning by enabling devices or systems to refine a shared model while preserving data locally. It works by acquiring the current model, enhancing it with local data, and transmitting condensed updates to a central model, which then merges them for improvement. Although FL enhances privacy by training individual models with local data, no system is entirely immune to vulnerabilities.

This approach allows multiple entities to refine machine learning models collaboratively without exchanging raw data, reducing the need for centralized storage. Google employs federated learning in Gboard on Android to enhance query suggestion models. With FL, collaborative model training across users’ devices is possible without raw data leaving the devices, unlocking opportunities for insights from diverse sources.

The proliferation of interconnected devices, including phones and sensors, presents vast potential. Federated learning enables collaboration to understand human mobility and health impacts. It extends to organizations like hospitals and pharmaceutical companies, facilitating insights into patient outcomes and drug development without disclosing sensitive data.

Federated learning supports large-scale modelling of complex systems, from urban mobility to climate change, empowering collaboration while preserving data ownership.

Conclusion : How Alliance PRO Can Empower Your Business

At Alliance PRO, we understand the critical importance of privacy in today’s data-driven ecosystem.  With our expertise in privacy-enhancing technologies, including differential privacy, federated learning, and homomorphic encryption, we stand ready to assist you in navigating the challenges.

Our team is at the forefront of advancements in privacy technology, ensuring that we can swiftly deploy the latest innovations as they emerge. We offer tailored solutions to enable the safe sharing of private data within and beyond your organization, facilitating advanced modelling while preserving privacy.

Moreover, we specialize in enhancing the quality of underpinning data sources and data management practices, making the utilization of privacy-preserving technologies feasible for your organization. With Alliance PRO by your side, you can confidently leverage the power of data while safeguarding privacy and maintaining the integrity of your operations.

Partner with us today and embark on a journey towards enhanced data privacy and unparalleled business success.