Machine learning promotes the continuous development of signal processing in
various fields, including network traffic monitoring, EEG classification, face
identification, and many more. However, massive user data collected for
training deep learning models raises privacy concerns and increases the
difficulty of manually adjusting the network structure. To address these
issues, we propose a privacy-preserving neural architecture search (PP-NAS)
framework based on secure multi-party computation to protect users' data and
the model's parameters/hyper-parameters. PP-NAS outsources the NAS task to two
non-colluding cloud servers for making full advantage of mixed protocols
design. Complement to the existing PP machine learning frameworks, we redesign
the secure ReLU and Max-pooling garbled circuits for significantly better
efficiency ($3 \sim 436$ times speed-up). We develop a new alternative to
approximate the Softmax function over secret shares, which bypasses the
limitation of approximating exponential operations in Softmax while improving
accuracy. Extensive analyses and experiments demonstrate PP-NAS's superiority
in security, efficiency, and accuracy.

使用安全多方计算方法构建隐私保护的神经架构搜索框架，其中使用重新设计的 ReLU 和 Max-pooling 保混乱电路以及对秘密共享的 Softmax 函数的新替代方法，分析和实验表明其在安全性，效率和准确性方面具有优越性。

基于隐私保护的神经架构搜索

Towards Privacy-Preserving Neural Architecture Search

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.

Chameleon 是一个独特的混合框架，它通过组合基于加性秘密共享的协议和通用秘密函数评估协议的优点来实现双方合作计算功能；其支持具有签名定点数的向量点积，可用于高效加密数据的挖掘和分类。研究结果显示，与 Microsoft CryptoNets 和 MiniONN 相比，Chameleon 的执行速度分别快 133 倍和 4.2 倍。