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。

Chameleon: 机器学习应用的混合安全计算框架

Chameleon: A Hybrid Secure Computation Framework for Machine Learning  Applications

We examine the combination of two directions in the field of privacy
concerning computations over distributed private inputs - secure function
evaluation (SFE) and differential privacy. While in both the goal is to
privately evaluate some function of the individual inputs, the privacy
requirements are significantly different. The general feasibility results for
SFE suggest a natural paradigm for implementing differentially private analyses
distributively: First choose what to compute, i.e., a differentially private
analysis; Then decide how to compute it, i.e., construct an SFE protocol for
this analysis.
We initiate an examination whether there are advantages to a paradigm where
both decisions are made simultaneously. In particular, we investigate under
which accuracy requirements it is beneficial to adapt this paradigm for
computing a collection of functions including binary sum, gap threshold, and
approximate median queries. Our results imply that when computing the binary
sum of $n$ distributed inputs then:
* When we require that the error is $o(\sqrt{n})$ and the number of rounds is
constant, there is no benefit in the new paradigm.
* When we allow an error of $O(\sqrt{n})$, the new paradigm yields more
efficient protocols when we consider protocols that compute symmetric
functions.
Our results also yield new separations between the local and global models of
computations for private data analysis.

研究如何同时应用 SFE 和差分隐私来计算分布式私人输入的函数，比较不同误差要求下计算二进制和、间隔阈值、近似中位数等函数的新模式和已有模式的效率，并发现新模式对于对称函数具有更好的效率，同时也提供了隐私数据分析的全局模型和局部模型的新分离结果。