量量测量 (Quantum Measurement Adversary)

Multi-source-extractors are functions that extract uniform randomness from multiple (weak) sources of randomness. Quantum multi-source-extractors were considered by Kasher and Kempe (for the quantum-independent-adversary and the quantum-bounded-storage-adversary), Chung, Li and Wu (for the general-entangled-adversary) and Arnon-Friedman, Portmann and Scholz (for the quantum-Markov-adversary). One of the main objectives of this work is to unify all the existing quantum multi-source adversary models. We propose two new models of adversaries: 1) the quantum-measurement-adversary (qm-adv), which generates side-information using entanglement and on post-measurement and 2) the quantum-communication-adversary (qc-adv), which generates side-information using entanglement and communication between multiple sources. We show that, 1. qm-adv is the strongest adversary among all the known adversaries, in the sense that the side-information of all other adversaries can be generated by qm-adv. 2. The (generalized) inner-product function (in fact a general class of two-wise independent functions) continues to work as a good extractor against qm-adv with matching parameters as that of Chor and Goldreich. 3. A non-malleable-extractor proposed by Li (against classical-adversaries) continues to be secure against quantum side-information. This result implies a non-malleable-extractor result of Aggarwal, Chung, Lin and Vidick with uniform seed. We strengthen their result via a completely different proof to make the non-malleable-extractor of Li secure against quantum side-information even when the seed is not uniform. 4. A modification (working with weak sources instead of uniform sources) of the Dodis and Wichs protocol for privacy-amplification is secure against active quantum adversaries. This strengthens on a recent result due to Aggarwal, Chung, Lin and Vidick which uses uniform sources.

翻译：多源提取器是从多种(弱)随机性来源中提取统一随机性的功能。 Kasher 和 Kempe (量独立偏差和量储存偏差)、 Chung、 Li 和 Wu (一般缠绕偏差) 和 Arnon-Friedman、 Portmann 和 Scholz (对于量子- Markov-adversal-alversic ) 。这项工作的主要目标之一是整合所有现有的定量多源对立模型。我们提出了两种对手的新模型:1 量独立偏差和量存储偏差(qm-advy) 、 Chung、 Li和 Wu(对于一般偏差-adversal-adversal ) 和 Arnon-Fridial-forman (对于多源之间的混结和沟通) 。我们显示, Qm-adv 是所有已知对手中最强的相对立方( ) 而非边际对立模型的匹配结果。我们觉得, Q- flick- freal- freal- fal- fal- fal- fal- flight- fal- flight- flick- real- real- real- real- real- real- real- real- real- real- real- real- real- sal- sal- real- y- six- maxlate- six to a mat- sal- supt- supt- supt- six- slate- supt- y- six- sal- slate- y- sal- sal- sal- sal- y- y- six- sal- laut- y- y- y- sal- y- y) y- y- y) 使使所有使所有使出出出出出出出出出一个非和G- 和整个一个和整个一个- 和整个结果。