Implementing Entropically Secure Encryption for Securing Sensitive Personal Health Data

Concetti Chiave

Entropically Secure Encryption (ESE) can provide unconditional security with shorter keys compared to the One-Time Pad, making it a practical encryption scheme for securing sensitive personal health data such as X-ray images and human genome data.

Sintesi

The paper presents the first implementation of Entropically Secure Encryption (ESE) for bulk encryption. ESE provides unconditional security with shorter keys compared to the One-Time Pad (OTP) by leveraging the entropy of the plaintext.
The key computational bottleneck for bulk ESE is the multiplication of large binary polynomials, which the authors address by developing a new algorithm called "simplemult" that outperforms existing libraries like gf2x. They also implement an efficient reduction algorithm for modular reduction.
The authors investigate two use cases - X-ray images and human genome data - and estimate the entropy of the data using compression methods. For X-ray images, they find that ESE can reduce the key length by 93% compared to OTP. For human genome data, the key length reduction is less significant due to the high entropy of the data, but ESE can still provide practical encryption speeds.
The authors also discuss the potential of integrating ESE with Quantum Key Distribution (QKD) to achieve full information-theoretic security of the communication channel, as the key consumption rate of ESE can be matched with the key generation rate of QKD in certain scenarios.
Overall, the paper demonstrates the feasibility of implementing ESE for securing sensitive personal health data and highlights the potential benefits of combining ESE with QKD for end-to-end information-theoretic security.

Statistiche

The average JPEG-LS-compressed X-ray file size is 5 MB, while the average estimated Shannon entropy for a file is approximately 4.62 MB.
The average compressed file size for human genome data using the Spring compression tool is around 6.49 GB.
The standard deviation of the compression ratio for human genome files is around 0.006.

Citazioni

"Entropically Secure Encryption (ESE) offers unconditional security with shorter keys compared to the One-Time Pad."
"If the plaintext has at least t collision entropy, then it can be encrypted using a key with a length l of l = n-t + 2 log 1/ε, where n is the message length and ε is the security parameter."
"We have implemented the entire ESE scheme by integrating our multiplication algorithm simplemult, the t_redBCP reduction, and the final XOR operation of the message and the expanded key."

Approfondimenti chiave tratti da

Implementation of Entropically Secure Encryption: Securing Personal Health Data

by Mehm... alle arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16857.pdf

Domande più approfondite

How can the entropy estimation process be further improved to provide tighter bounds on the required key length for ESE

To improve the entropy estimation process for tighter bounds on the required key length in Entropically Secure Encryption (ESE), several approaches can be considered:

Advanced Compression Techniques: Explore more sophisticated compression algorithms specifically tailored for the type of data being encrypted. By utilizing domain-specific compression methods, a more accurate estimation of the collision entropy can be achieved.
Machine Learning Models: Implement machine learning models to analyze the data and predict its entropy more accurately. Training models on a diverse set of data samples can enhance the precision of entropy estimation.
Dynamic Entropy Estimation: Develop algorithms that adaptively adjust the estimated entropy based on the characteristics of the data being encrypted. This dynamic approach can provide more precise key length requirements.
Statistical Analysis: Utilize statistical methods to analyze the distribution of the data and infer its entropy. By incorporating statistical techniques, a more robust estimation of entropy can be obtained.
Hybrid Approaches: Combine multiple entropy estimation techniques, such as compression-based methods, statistical analysis, and machine learning, to create a comprehensive and accurate estimation framework for determining the key length in ESE.

What other types of sensitive personal data, beyond medical images and genome data, could benefit from the application of ESE

Beyond medical images and genome data, various other types of sensitive personal data can benefit from the application of Entropically Secure Encryption (ESE). Some examples include:

Financial Data: Personal financial information, such as banking records, investment portfolios, and transaction details, can benefit from ESE to ensure secure and confidential storage and transmission.
Legal Documents: Confidential legal documents, including contracts, intellectual property records, and court filings, can be safeguarded using ESE to prevent unauthorized access and maintain data integrity.
Government Records: Sensitive government data, such as classified documents, national security information, and citizen records, can be protected with ESE to mitigate security risks and ensure privacy.
Research Data: Research institutions handling sensitive research data, including proprietary findings, clinical trial results, and intellectual property, can utilize ESE for secure data storage and sharing.
Personal Communications: Encrypted messaging platforms, email communications, and voice calls can leverage ESE to enhance privacy and confidentiality in personal interactions.

How can the performance of the ESE implementation be further optimized, especially for the reduction step, to achieve even higher encryption rates

To further optimize the performance of the ESE implementation, especially for the reduction step, the following strategies can be employed:

Algorithmic Enhancements: Refine the reduction algorithm to improve its efficiency and reduce computational overhead. Implement optimized algorithms that leverage parallel processing and efficient data structures for faster reduction operations.
Hardware Acceleration: Utilize hardware acceleration techniques, such as GPU computing or specialized hardware accelerators, to offload computationally intensive tasks like reduction operations and enhance overall encryption rates.
Code Optimization: Conduct thorough code optimization to streamline the implementation of the reduction step, including minimizing redundant operations, reducing memory access latency, and enhancing algorithmic efficiency.
Parallelization: Implement parallel processing techniques to parallelize the reduction step across multiple cores or threads, maximizing computational resources and improving encryption rates.
Benchmarking and Profiling: Continuously benchmark and profile the ESE implementation to identify performance bottlenecks, optimize critical sections of the code, and fine-tune parameters for enhanced encryption speed and efficiency.