DeepSecure: Scalable Provably-Secure Deep Learning (1705.08963v1)

Abstract: This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves more than 58-fold higher throughput per sample compared with the best-known prior solution. In addition to our optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of deep learning. Extensive evaluations of various DL applications demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology. This paper also provides mechanisms to securely delegate GC computations to a third party in constrained embedded settings.

• The paper presents a provably-secure deep learning framework that secures model parameters and client data using Yao’s Garbled Circuit protocol.
• It employs novel optimizations like data preprocessing and low-overhead Boolean circuits to reduce communication costs and accelerate secure computations.
• The framework demonstrates up to 82x speed improvement over homomorphic encryption, enabling secure inference in resource-limited environments.

An Overview of DeepSecure: Provably-Secure Deep Learning Framework

The paper introduces DeepSecure, an innovative framework designed to facilitate privacy-preserving deep learning (DL) computations, employing Yao’s Garbled Circuit (GC) protocol. DeepSecure is differentiated by its ability to provide robust security for both DL model parameters and client data, without compromising on scalability or accuracy. Herein, I will explore the structural and functional elements of DeepSecure, highlight its empirical performance outcomes, and discuss its potential implications and future pathways for research.

Architectural and Methodological Insights

DeepSecure targets the intersection of privacy and efficiency, tackling challenges intrinsic to executing DL models on sensitive data while ensuring the confidentiality of both client-held data and server-held DL model parameters. The primary contribution lies in leveraging the GC protocol, a foundational cryptographic method, to secure function evaluation processes in a manner entirely agnostic to the function structure used in DL models.

One of the core strengths of DeepSecure is its scalability, derived from novel optimizations tailored for the GC protocol. These include a significant reduction in communication overhead, achieved through a combination of optimized custom circuits and data preprocessing techniques that transform data into lower-dimensional subspaces. This preprocessing not only reduces communication costs but also compliments network sparsity, systematically trimming unnecessary neural network computations.

The authors propose a noteworthy enhancement for resource-limited environments through secure outsourcing to a non-colluding proxy server. This feature is quintessential for deployment in constrained devices like smartphones and wearable technology. The outsourcing design hinges on an XOR-sharing technique, reinforcing the system's overall security model against potential breaches of data confidentiality.

Performance Analysis

DeepSecure's performance evaluations, conducted across different DL networks and benchmark datasets, underscore its striking efficiency. When compared to existing homomorphic encryption-based solutions, DeepSecure achieves up to 82 times improvement in execution time across various benchmarks, owing primarily to its preprocessing enhancements. This improvement outstrips existing methodologies by sidestepping the limitations imposed by homomorphic encryption, notably its accuracy-privacy trade-off and considerable latency issues.

The implementation specifics are meticulously optimized, utilizing modern synthesis tools to yield low-overhead Boolean circuits, which directly translate to a reduction in GC execution costs. The paper provides explicit quantitative characterizations of computational and communicational savings, reinforcing DeepSecure's practicability and robustness.

Implications and Future Directions

The advent of DeepSecure presents substantial implications for privacy-preserving computations in distributed DL settings. Practically, it empowers DL as-a-service paradigms, enabling secure cloud-based inference without the traditional hurdles of computational overhead or privacy compromise. Theoretically, the work predicates further exploration in secure computing frameworks employing garbled circuits, potentially sparking innovations in algorithmic efficiency and cryptographic protocol design.

Future developments could focus on extending DeepSecure’s foundational work, investigating more comprehensive models of adversarial behavior beyond the Honest-but-Curious model, and integrating dynamic data streams. Additionally, refining preprocessing strategies to incorporate emerging trends in federated learning may present new avenues for optimizing performance and capability.

Conclusion

In conclusion, DeepSecure harnesses the potential of GC protocol optimizations and intelligent data manipulation techniques to establish a groundbreaking architecture in secure DL execution. Its ability to maintain high throughput while securing data and models makes it a highly viable solution in the contemporary landscape of privacy-sensitive AI deployments. The paper significantly contributes to the broader conversation around balancing security and efficiency in AI, paving the way for future research and applications in secure, distributed deep learning.