CN113691369A - Optical fiber channel safe transmission system based on neural network - Google Patents
Optical fiber channel safe transmission system based on neural network Download PDFInfo
- Publication number
- CN113691369A CN113691369A CN202110772016.4A CN202110772016A CN113691369A CN 113691369 A CN113691369 A CN 113691369A CN 202110772016 A CN202110772016 A CN 202110772016A CN 113691369 A CN113691369 A CN 113691369A
- Authority
- CN
- China
- Prior art keywords
- neural network
- data
- key
- pseudo
- transmission system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 38
- 230000005540 biological transmission Effects 0.000 title claims abstract description 34
- 239000013307 optical fiber Substances 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 10
- 239000000835 fiber Substances 0.000 claims description 18
- 230000003321 amplification Effects 0.000 claims description 8
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 238000012805 post-processing Methods 0.000 claims description 5
- 239000000523 sample Substances 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000003062 neural network model Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 210000002569 neuron Anatomy 0.000 description 3
- 230000010287 polarization Effects 0.000 description 3
- 239000006185 dispersion Substances 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0861—Generation of secret information including derivation or calculation of cryptographic keys or passwords
- H04L9/0869—Generation of secret information including derivation or calculation of cryptographic keys or passwords involving random numbers or seeds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/60—Receivers
- H04B10/66—Non-coherent receivers, e.g. using direct detection
- H04B10/69—Electrical arrangements in the receiver
- H04B10/691—Arrangements for optimizing the photodetector in the receiver
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/80—Optical aspects relating to the use of optical transmission for specific applications, not provided for in groups H04B10/03 - H04B10/70, e.g. optical power feeding or optical transmission through water
- H04B10/85—Protection from unauthorised access, e.g. eavesdrop protection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0618—Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation
- H04L9/0631—Substitution permutation network [SPN], i.e. cipher composed of a number of stages or rounds each involving linear and nonlinear transformations, e.g. AES algorithms
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/0852—Quantum cryptography
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Electromagnetism (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
- Optical Communication System (AREA)
Abstract
本发明公开了一种基于神经网络的光纤信道安全传输系统。该系统分为五个步骤：1)合法方向彼此发送一段约定好的发送数据并在接收机处做数据接收；2)合法方对接收数据做数据后处理并在本地结合发送数据和处理后的接收数据训练神经网络；3)发送端在本地生成一段伪密钥并与训练好的神经网络结合获得密钥，然后加密明文；4)发送端组合伪密钥和密文成新数据并传输到接收端，接收端接收时采用信道补偿算法；5)接收端将收到的伪密钥结合训练好的神经网络生成密钥并解密明文。该安全传输系统不仅实现了通密同传、动态调整密钥、并且依然保持安全当合法方所用光纤的长度被破解，即该系统的密钥空间非常大。
The invention discloses an optical fiber channel safe transmission system based on neural network. The system is divided into five steps: 1) The legitimate parties send a piece of agreed transmission data to each other and receive the data at the receiver; 2) The legitimate parties process the received data and combine the sent data and the processed data locally. Receive data to train the neural network; 3) The sender generates a pseudo-key locally and combines it with the trained neural network to obtain the key, and then encrypts the plaintext; 4) The sender combines the pseudo-key and the ciphertext into new data and transmits it to the receiver The receiver adopts the channel compensation algorithm when receiving; 5) The receiver combines the received pseudo key with the trained neural network to generate the key and decrypts the plaintext. The secure transmission system not only realizes the simultaneous interpretation of passwords and the dynamic key adjustment, but also maintains security when the length of the optical fiber used by the legitimate party is cracked, that is, the key space of the system is very large.
Description
Technical Field
The invention relates to an information security technology, in particular to a scheme for preventing an illegal eavesdropper from eavesdropping on transmission information under optical fiber channel transmission.
Background
Optical transmission is widely used in personal, commercial, and military communications because of its advantages such as high speed, large bandwidth, and long reach. However, with the 5G era and the advent of various intelligent devices, the data requirement in the optical link has increased explosively, so that the data security transmission of the optical link becomes more and more important. In point-to-point transmission systems, optical fiber is often used as the system communication link, but optical fiber is very vulnerable to many eavesdropping, so how to guarantee the secure transmission of optical communication systems attracts more and more attention.
The traditional security technology mainly guarantees the security of the system by means of the complexity of a physical layer and a high-level algorithm, but along with the development of a quantum computer, the traditional security technology is no longer secure. The theoretically completely safe quantum key technology also has the disadvantages of short transmission distance, low key generation rate, expensive device and the like. Although the chaotic security system can improve the security of data transmission, the key space of the system is relatively small, i.e. the security is not high, because the security is established in the randomness and unpredictability of the initial value, so that the secure transmission scheme based on the physical layer becomes a hot field of research in recent years.
In recent years, many physical layer-based security schemes have been proposed, such as random phase fluctuation based on polarization-maintaining fibers, polarization film dispersion based on random spliceable maintaining fibers, characteristics of ultra-long fiber lasers, random film-mixing extraction keys in multimode fibers. Although these schemes can improve the confidentiality and privacy of data transmission from the perspective of the physical layer, these schemes do not consider that the received data of the legitimate party is not completely consistent due to the non-ideal environment, a post-processing algorithm is required to perform the consistency of the received data of the legitimate party and the differential amplification of the received data of the illegitimate party, the common transmission of information and a secret key cannot be realized, and the secure communication cannot be performed even when the length of a legitimate channel optical fiber is matched by the eavesdropping party.
Neural networks have been proven to fit many data curves well even with only one hidden layer, so that neural networks are increasingly studied in channel modeling, and we need to study a secure transmission system that is safely established in channel characteristic estimation neural networks, considering that the polarization film dispersion of fiber channels is a variable and the time for matching the legal fiber length by eavesdropping method cannot be made zero.
Disclosure of Invention
In order to solve the technical problem, the invention provides a fiber channel secure transmission system based on a neural network, and a legal party can locally obtain a neural network model which cannot be obtained by an illegal eavesdropping party through a method for locally estimating the characteristics of a transmission channel through the neural network, so that the eavesdropping party cannot obtain a secret key used for plaintext encryption.
The invention provides a fiber channel safe transmission system based on a neural network, which is characterized in that,
firstly, a legal method trains a neural network model locally, then a sending end generates a section of pseudo key locally, a real key is generated by using the pseudo key and the neural network model, a plaintext is encrypted by using the key, a ciphertext and the pseudo key are transmitted to a receiving end after encryption, and the receiving end receives and decrypts data.
The training neural network model refers to that detection data and data of received data which are sent by a sending end and are subjected to post-processing are used as training data of a neural network, wherein the sent data are used as input data, and the received data which are subjected to post-processing are used as tag data. The post-processing is used for received data consistency of a legal party and received data differential amplification of an illegal eavesdropper, because a fiber channel is not ideal, the received data after the same transmitted data of the legal party is transmitted are not necessarily identical, so that the received data of the legal party is required to be consistent by adopting information negotiation, and in order to further expand the data difference of the legal party and the illegal party, the received data differential amplification of the legal party and the illegal eavesdropper is carried out by adopting privacy amplification after the information negotiation.
The generation of a pseudo key is to locally generate a pseudo random number as a pseudo key before the encryption is performed at the receiving end.
The generation of the real key means that the pseudo key is input into a local trained neural network, and data output by the neural network is the real key at the moment.
The encryption of the plaintext refers to the encryption of the plaintext needing to be transmitted in a secret manner according to a secret key.
The communication means that the sending end combines the ciphertext and the pseudo key into a group of new data, and then transmits the new data to the receiving end in a standard single mode optical fiber.
The receiving and decrypting of the data means that a receiving end receives lossless signals through a channel compensation algorithm, then ciphertext and a pseudo key are respectively obtained through a predetermined combination mode, the pseudo key is input into a locally trained neural network to generate a key, and a process opposite to encryption is carried out on the ciphertext according to the key to obtain a plaintext.
Compared with the existing safe transmission system, the system not only realizes the simultaneous transmission of the open cipher, one-time cipher and real-time key adjustment, but also can ensure the safety of most conditions of the system under the condition that the eavesdropping party perfectly matches the length of the legal channel optical fiber, and because of the time-varying property and the randomness of the polarization membrane coefficient in the channel and the diversity of the neural network structure, the key space of the scheme is very large and is difficult to crack.
In the embodiment of the method, an implementation case of a fiber channel secure transmission system based on a neural network is given.
Drawings
The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail exemplary embodiments thereof with reference to the attached drawings, in which:
fig. 1 is a diagram of a secure fiber channel transmission system based on a neural network according to an embodiment of the present invention.
Fig. 2 is a flow chart of the secure transmission of the fiber channel based on the neural network according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of channel characteristic detection according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of the training of the neural network for channel characteristic estimation according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a pseudo key generation key according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of communication according to an embodiment of the present invention.
Detailed description of the invention
The present solution is described in further detail below with reference to the accompanying drawings
Fig. 1 and fig. 2 are a diagram and a flowchart of a secure fiber channel transmission system based on a neural network according to an embodiment of the present invention, in the secure fiber channel transmission system, the total steps are five:
the first step is that the legitimate users Alice and Bob in step S201 simultaneously send a predetermined probing signal data to each other, where the probing signal is a 40Gbps NRZ electrical signal, and in order to amplify the loss of the channel, we set an EDFA of 20db and load the electrical signal onto the optical signal using MZM. The detection signal is transmitted through the local optical fiber and the exposed optical fiber and then received by the receiver, the received signal is subjected to photoelectric conversion through the photoelectric detector, and then digital signal processing is performed to obtain received data, as shown in fig. 3.
In the second step, the received signal of the legal party in step S202 needs to be post-processed, in this case, the information negotiation technique of Cascade is adopted, and in the information negotiation process, the legal party only transmits the parity value of the received signal on the common channel and corrects the local data. And after the information negotiation, the difference amplification of the eavesdropper and the legal party is carried out by adopting a hash function. In step S203, the local neural network is trained according to the probe signal sent in S201 and the post-processed received data obtained in S202, where the input data is the sent data and the tag data is the post-processed received data. As shown in fig. 4, a four-layer fully-connected layer neural network is used in this case, in which the number of neurons in the input layer is 51, which represents that a single symbol in the channel will receive interference from the surrounding 50 symbols, the numbers of neurons in the second, third and fourth hidden layers are 128 × 8, 128 × 4 and 128 × 2, respectively, and the number of neurons in the output layer is 2, which represents the probability of two symbols in NRZ. The activation functions of the hidden layer and the output layer of the neural network are Relu and Softmax, batchsize and epoch are all 100, the learning rate is 0.0001, the training set and the testing set are all 4096 bits, Adam is used as a training optimizer, a dropout function is not adopted, the error function is a cross dead error function for characterizing two probability similarities in an information theory, and a flow chart of the hidden layer is also shown in figure 4.
The third step first needs to generate a pseudo random number, i.e. a pseudo key, locally at the sending end in step S204, and step S205 is to generate a real key by using the pseudo key through the neural network model trained in step S203, as shown in fig. 5.
In the fourth step, step S206 is first performed, that is, the sending end encrypts plaintext information to be sent by using the locally generated key, in this case, a standard AES encryption algorithm is used. After encryption, the ciphertext and the pseudo key need to be sent to the receiving end together, in the scheme, a combination mode that the first half is the ciphertext and the second half is the pseudo key is adopted, and the combined data is transmitted to the receiving end through a standard single-mode optical fiber. In step S207, the receiving end may obtain a lossless transmission signal after compensating the transmission channel, and may obtain a lossless ciphertext and a pseudo key by using a predetermined method, as shown in fig. 6.
Step S208 is firstly needed to be carried out in the fifth step, after the receiving end receives the ciphertext and the pseudo key, the receiving end needs to input the pseudo key into a neural network model which is trained locally by the receiving end, and the output is the real key. Step S209 needs to decrypt the plaintext information sent from the sender by combining the key generated in step S208 with the ciphertext obtained in step S207.
The above description is only an example of the present disclosure, and is not intended to limit the present disclosure, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (5)
1. A secure transmission system of optical fiber channel based on neural network, characterized in that:
firstly, legal parties need to send detection signals agreed in advance, then, signals received by the legal parties are subjected to a unification operation according to a negotiation algorithm, then, a hash function is used for carrying out privacy amplification operation on data, and then, the detection data and the post-processed received data are used as training data of a local neural network. After the neural network is trained, the sending end locally generates a pseudo-random number as a pseudo-secret key, and the pseudo-secret key is input into the trained neural network to generate a real secret key. At the moment, a sending end has a secret key and plaintext information to be transmitted, the sending end encrypts the plaintext according to the secret key, the ciphertext and the pseudo secret key are combined into a group of new data after encryption, the new data are transmitted to a receiving end through a standard single mode optical fiber, the receiving end can obtain a lossless pseudo random number and a lossless ciphertext after channel compensation, then the pseudo secret key is input into a trained neural network to generate a secret key, and the secret key is used for decrypting the ciphertext to obtain the plaintext.
2. The secure fiber channel transmission system based on neural network as claimed in claim 1, wherein the information negotiation operation is used to ensure the data consistency of legal party, the privacy amplification operation is used to ensure the difference between legal party and illegal party, and the information negotiation operation and the privacy amplification operation together constitute the data post-processing procedure.
3. The secure fibre channel transmission system based on neural network as claimed in claim 1, wherein the neural network used in the system is mainly used for estimating channel characteristics, wherein the input data of the neural network is probe data, and the tag data is post-processed received data.
4. The secure fibre channel transmission system based on neural network as claimed in claim 1, wherein the pseudo-key generation key requires inputting the pseudo-key into the neural network, and the key is outputted from the neural network.
5. The secure fiber channel transmission system based on neural network as claimed in claim 1, wherein the combination form of the pseudo key and the plaintext is various and is not limited, and only needs to be agreed by a legal party in advance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110772016.4A CN113691369B (en) | 2021-07-08 | 2021-07-08 | A Fibre Channel Secure Transmission System Based on Neural Network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110772016.4A CN113691369B (en) | 2021-07-08 | 2021-07-08 | A Fibre Channel Secure Transmission System Based on Neural Network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113691369A true CN113691369A (en) | 2021-11-23 |
| CN113691369B CN113691369B (en) | 2022-09-27 |
Family
ID=78576788
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110772016.4A Active CN113691369B (en) | 2021-07-08 | 2021-07-08 | A Fibre Channel Secure Transmission System Based on Neural Network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113691369B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108365951A (en) * | 2018-01-19 | 2018-08-03 | 中国人民解放军陆军工程大学 | High-consistency physical key generation method based on neural network |
| US20200304292A1 (en) * | 2020-05-18 | 2020-09-24 | Timofey Mochalov | Method for protecting data transfer using neural cryptography |
| US20200334542A1 (en) * | 2018-01-02 | 2020-10-22 | Nokia Technologies Oy | Channel modelling in a data transmission system |
| CN112422226A (en) * | 2020-11-11 | 2021-02-26 | 天津大学 | Key generation method and device based on polarization mode dispersion of optical fiber communication link |
| CN112906035A (en) * | 2021-03-24 | 2021-06-04 | 东南大学 | Deep learning-based frequency division duplex system key generation method |
-
2021
- 2021-07-08 CN CN202110772016.4A patent/CN113691369B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200334542A1 (en) * | 2018-01-02 | 2020-10-22 | Nokia Technologies Oy | Channel modelling in a data transmission system |
| CN108365951A (en) * | 2018-01-19 | 2018-08-03 | 中国人民解放军陆军工程大学 | High-consistency physical key generation method based on neural network |
| US20200304292A1 (en) * | 2020-05-18 | 2020-09-24 | Timofey Mochalov | Method for protecting data transfer using neural cryptography |
| CN112422226A (en) * | 2020-11-11 | 2021-02-26 | 天津大学 | Key generation method and device based on polarization mode dispersion of optical fiber communication link |
| CN112906035A (en) * | 2021-03-24 | 2021-06-04 | 东南大学 | Deep learning-based frequency division duplex system key generation method |
Non-Patent Citations (2)
| Title |
|---|
| ZHENG WAN: "《Secret Key Generation Scheme Based on Deep Learning in FDD MIMO Systems》", 《IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS》 * |
| 葛钊成: "神经网络与密码学的交叉研究", 《密码学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113691369B (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110225016B (en) | Data hidden transmission method based on block chain network | |
| EP2281361B1 (en) | Quantum key distribution involving moveable key device | |
| KR101314210B1 (en) | A method of User-authenticated Quantum Key Distribution | |
| CN106452750B (en) | A quantum encrypted communication method for mobile devices | |
| Lakshmi et al. | Comparison of classical and quantum cryptography using QKD simulator | |
| Wu et al. | Channel-based dynamic key generation for physical layer security in OFDM-PON systems | |
| CN112422226B (en) | Key generation method and device based on polarization mode dispersion of optical fiber communication link | |
| US20220294618A1 (en) | Improvements to qkd methods | |
| Al‐Ghamdi et al. | On the security and confidentiality of quantum key distribution | |
| Wang et al. | Data center secure communication via DNA hyperchaotic encryption | |
| Ikeda et al. | Two‐dimensional encryption system for secure free‐space optical communication of time‐series data streams | |
| CN113691369B (en) | A Fibre Channel Secure Transmission System Based on Neural Network | |
| Buskila et al. | Secure communication in fiber optic systems via transmission of broad-band optical noise | |
| CN112468236B (en) | An information security transmission method based on photon noise aliasing multi-ary transform | |
| Eriksson et al. | Secret key rates for intensity-modulated dual-threshold detection key distribution under individual beam splitting attacks | |
| CN119256518A (en) | Method and system for performing secure transactions | |
| Al-Mohammed et al. | New way to generating and simulation QKD | |
| Farrugia et al. | PQC and QKD are both required to enable a quantum-safe future | |
| Harrison et al. | Security through uncertainty | |
| CN109981270B (en) | Peer-to-peer file transfer method based on quantum key synchronous destruction technology | |
| Bhosale et al. | Polarization based secure AES key transmission over optical fiber | |
| Yalla et al. | Secure method of communication using Quantum Key Distribution | |
| Wu et al. | Channel characteristics estimation based on a secure optical transmission system with deep neural networks | |
| Sharma et al. | Transmission and control for QKD in online banking systems | |
| Iwakoshi | Potentially Information-theoretic Secure Y00 Quantum Stream Cipher with Limited Key Lengths beyond One-Time Pad |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |