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BLAZE: Blazing Fast Privacy-Preserving Machine Learning (2005.09042v1)

Published 18 May 2020 in cs.CR and cs.LG

Abstract: Machine learning tools have illustrated their potential in many significant sectors such as healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential nature of the data, in such sectors, raise natural concerns for the privacy of data. This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed. Typically, ML techniques require large computing power, which leads clients with limited infrastructure to rely on the method of Secure Outsourced Computation (SOC). In SOC setting, the computation is outsourced to a set of specialized and powerful cloud servers and the service is availed on a pay-per-use basis. In this work, we explore PPML techniques in the SOC setting for widely used ML algorithms-- Linear Regression, Logistic Regression, and Neural Networks. We propose BLAZE, a blazing fast PPML framework in the three server setting tolerating one malicious corruption over a ring (\Z{\ell}). BLAZE achieves the stronger security guarantee of fairness (all honest servers get the output whenever the corrupt server obtains the same). Leveraging an input-independent preprocessing phase, BLAZE has a fast input-dependent online phase relying on efficient PPML primitives such as: (i) A dot product protocol for which the communication in the online phase is independent of the vector size, the first of its kind in the three server setting; (ii) A method for truncation that shuns evaluating expensive circuit for Ripple Carry Adders (RCA) and achieves a constant round complexity. This improves over the truncation method of ABY3 (Mohassel et al., CCS 2018) that uses RCA and consumes a round complexity that is of the order of the depth of RCA. An extensive benchmarking of BLAZE for the aforementioned ML algorithms over a 64-bit ring in both WAN and LAN settings shows massive improvements over ABY3.

Citations (164)

Summary

  • The paper introduces BLAZE, a three-layer framework that significantly optimizes PPML operations by reducing communication complexity and latency.
  • It implements efficient privacy-preserving primitives for key ML tasks such as linear/logistic regression and neural network inference.
  • BLAZE ensures fairness by equitably distributing outputs among honest parties and outperforms benchmarks in both WAN and LAN environments.

An Evaluation of BLAZE: Fast Privacy-Preserving Framework for Machine Learning

Abstract

The paper "BLAZE: Blazing Fast Privacy-Preserving Machine Learning" provides an in-depth examination of a structured framework aimed at the efficacious transformation of Machine Learning (ML) models into privacy-preserving systems. With a concentration on secure outsourced computation settings, the paper discusses a three-server model in which one server can be maliciously corrupt. The essence of this research lies in its articulation of efficiency within privacy-preserving machine learning (PPML) operations, achieved by addressing both linear regression, logistic regression training, and neural networks inference.

Highlights and Contributions

1. Framework Design and Performance:

The paper introduces BLAZE, a framework structured to enhance both computational and communication efficiency in PPML. The implementation successfully optimizes key operations such as dot products and truncation, diverting from extant approaches through the novel utilization of efficient privacy-preserving primitives resulting in communication complexity that is independent of vector size.

2. Layered Approach:

BLAZE is stratified into three layers—starting from primary building blocks at Layer-I (multiplication, bit extraction, and bit-to-arithmetic conversion) to Layer-II building blocks (dot products, truncation, and activation functions), culminating in Layer-III applications for ML algorithms. This hierarchical arrangement underscores the architectural robustness required for scalable implementations in real-world scenarios.

3. Security and Fairness:

The paper expands its focus beyond efficiency, underlining the importance of fairness in PPML protocols. By ensuring all honest parties get result outputs equivalently to any adversary-led corrupt parties, BLAZE upholds a principle crucial for the integrity and trust in PPML adoption.

4. Benchmarking and Comparative Analysis:

Through rigorous benchmarking over both WAN and LAN settings, BLAZE demonstrates substantial gains in throughput over previous frameworks like ABY3 and ASTRA. The paper meticulously details performance gains, emphasizing the exceedingly reduced latency and high-throughput capabilities on lower bandwidth internet structures—a significant practical implication for global implementation.

Implications and Future Directions

Practical Considerations:

BLAZE effectively responds to privacy standards such as GDPR, advocating PPML's evolution amidst heightened data privacy demands. With its profound reduction in computational overhead, BLAZE emerges as a candidate for wide-scale adoption in industries dealing with sensitive data metrics.

Potential Extensions:

The paper acknowledges areas for future investigation, particularly the enhancement of neural network training within privacy-preserving environments. It also ventures into the plausible amalgamation with Trusted Execution Environments (TEE), anticipating further improvements in the framework's efficiency and security posture.

Theoretical Developments:

From a cryptographic perspective, the novel strategies employed for secure operations over ring structures are notable. These innovations pave the way for enriched explorations into more complex ML model privacy and secure computations, expanding theoretical understandings and practical applications alike.

Conclusion

"BLAZE: Blazing Fast Privacy-Preserving Machine Learning" presents a compelling narrative in PPML, addressing not only speed and efficiency but the pivotal role of fairness in data security. It is poised as a leading framework, encouraging subsequent research and practical implementations to meet the concurrent demand for robust, privacy-centric machine learning solutions. The paper signals meaningful advancements for theoretical cryptography and privacy-preserving methodologies, challenging traditional paradigms and inviting future innovations in this promising domain.