论文阅读笔记-更新中7.17-7.23
2017-07-19 20:49
351 查看
日期:2017-7-17
日期:2017-7-19
Field Switching in BGV-Style Homomorphic Encryption
日期:2017-7-21 讨论会
Fuzzy Identity-Based Encryption
Improved Short Lattice Signatures in the Standard
A Framework for Efficient and Composable Oblivious Transfer
标题 | 高效、可组合的不经意传输框架 |
引入的问题 | OT协议:允许一方(接收器),从另一方(发件人)准确地获取两个(或更多)值 。接收方使用其选择位(和CRS)来生成公共密钥和秘密密钥,并提供给发件人的公钥。发送方计算两个派生公共密钥(使用CRS),每个值在相应的派生密钥进行加密,暗文发送到接收器。最后,接收方使用其秘密密钥解密适当的值。 在实际运用上,OT protocols 在大型计算机的协议中无法证明其安全性,比如在selective-failure attacks中失效。 |
目标 | Secure and composable;Efficient;Generally realizable present a simple and novel framework。 |
技术思路 | 由Dual-mode cryptosystems保证OT协议的安全性: 1.messy mode:至少一个发件人的值被加密系统隐藏统计,意味着在unbounded cheating receiver攻击中统计安全。2:decryption mode:诚实接受者的选择位被基于秘钥隐藏统计。对应于每个潜在的选择位,连同两个妥善的分布式密钥生成公共密钥。这样就可以解密发件人的两个暗文,意味着统计安全性,以防止作弊甚至无限发件人。3:混乱模式+解密模式 双模式抽象,计算难以区分 |
技术细节 | our constructions guarantee that for any base key (the receiver’s message), at least one of the derived keys is messy.A novel part of our constructions is in the use of a trapdoor for efficiently identifying messy keys. For our DDH-based construction, we obtain a dual-mode cryptosystem via relatively straightforward abstraction and modification of prior protocols. specifically, we use a modification of Cocks’ identity-based cryptosystem [Coc01]. In both constructions, we have a precise characterization of messy keys and a trapdoor algorithm for identifying them. Our DDH construction transfers strings, while the QR and lattice constructions essentially allow only for single-bit transfers. |
成果和结论 | 1、a simple abstraction that we call adual-modecryptosystem. 2、we give a multi-bit version of Regev’s lattice-based cryptosystem whose time and space efficiency are smaller by a linear factor in the security parameter n. |
日期:2017-7-19
Field Switching in BGV-Style Homomorphic Encryption
标题 | BGV风格的同态加密中的字段交换 |
引入的问题 | 现有的同态加密在实际运用中代价过高。 |
目标 | we present a technique for reducing the dimension of the ciphertexts involved in the homomorphic computation of the lower levels of a circuit. 在低级别电路中,将参与同态计算的暗文降维。 Extending and improving the field switching procedure is the goal of our work. (field switching方法将高维的密文转为同信息的small-field密文)扩大和提高字段交换方法是我们的目标。 |
技术思路 | Step 1: Switching to a Small-Ring Secret Key 密钥交换操作,得到K上的big-field暗文,得到K’上small-field密钥s’∈K’(安全性由从K’到K的嵌入Ring-LWE问题证明) Steps 2 and 3: Mapping to the Small Field 2、将得到的暗文乘上一个环R的元,该元只依赖需要转换的明文子集 3、通过追踪K中的元,得到子域K’上的暗文,通过s’解密得到明文值 |
扩展学习 | RING-LWE. The ring learning with errors (RLWE) problem is built onthe arithmetic of polynomials with coefficients from a finite field.被规约到多项式环理想格中的近似最短向量问题。 Key switching. |
成果和结论 | We present a general field-switching transformation that can be applied to anycyclotomicnumber field, and works well in conjunction with packed ciphertexts. |
日期:2017-7-21 讨论会
Fuzzy Identity-Based Encryption
Identity-Based Encryption from Lattices in the Standard Model
Improved Short Lattice Signatures in the Standard
Model
相关文章推荐
- 论文阅读笔记:A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation
- 【论文阅读笔记】Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 中文推荐相关论文阅读笔记
- 论文阅读笔记-CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- SSD 论文(1)阅读笔记简化
- 论文阅读笔记: 2017 cvpr Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- 大教堂和集市--两种不同的软件开发模式 论文阅读笔记
- 论文阅读笔记3
- 论文阅读笔记 - YARN : Architecture of Next Generation Apache Hadoop MapReduceFramework
- Couple Net论文阅读笔记
- Deep Residual Learning for Image Recognition--ResNet论文阅读笔记
- 【论文阅读笔记】DEEP COMPRESSION:COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION...
- 【CS229 Lecture notes,Machine Learning,Andrew Ng 】阅读笔记(持续更新中...)
- 论文阅读笔记之How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source
- 计算机视觉-论文阅读笔记-基于高性能检测器与表观特征的多目标跟踪
- 关于 AlphaGo 论文的阅读笔记
- 论文阅读笔记(二)
- TAO: Facebook's Distributed Data Store for the Social Graph论文阅读笔记
- 全卷积(FCN)论文阅读笔记:Fully Convolutional Networks for Semantic Segmentation
- 【论文阅读笔记】CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning