Based on the principle of information theory, a novel scheme of unequal-interval frequency-hopping (FH) systems was proposed. For cases of spectrum overlapping systems and non-overlapping systems, the implementation m...Based on the principle of information theory, a novel scheme of unequal-interval frequency-hopping (FH) systems was proposed. For cases of spectrum overlapping systems and non-overlapping systems, the implementation methods were presented and the security performances were discussed theoretically. Firstly, the definitions of absolute and relative key amounts of FH systems, equal-interval and unequal-interval FH systems were given. Then, the absolute key amount and relative key amount were analyzed for equal-interval and unequal-interval FH systems. The results indicated that the absolute key amount had become the key point in improving the security and secrecy of FH systems, especially in today's epoch of highly developed computer science and IC design technology. Theoretical analysis and practical examples showed that the absolute key amount of unequal-interval FH systems was generally over two orders larger than that of equal-interval ones when spectrum overlapping was allowable. Therefore, there was great superiority in enhancing the security and secrecy for the scheme mentioned.展开更多
Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep ...Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.展开更多
Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sha...Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep learning.Concretely,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually.It also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model training.The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.展开更多
We present an all-optical chaotic multi-quantum-well (MQW) laser repeater system to be used in long-haul chaotic communications. Chaotic synchronization is achieved among transmitter, repeater, and receiver. Chaotic r...We present an all-optical chaotic multi-quantum-well (MQW) laser repeater system to be used in long-haul chaotic communications. Chaotic synchronization is achieved among transmitter, repeater, and receiver. Chaotic repeater communications with a sinusoidal signal of 0.2-GHz modulation frequency and a digital signal of 0.4-Gb/s bit rate are numerically simulated, respectively. Calculation results illustrate that the signals are well decoded by the chaotic repeaters. Its bandwidth and the characteristics at much high bit rate are also analyzed. Simulation shows that the repeater can improve decoding quality, especially in higher bit rate chaotic communications.展开更多
文摘Based on the principle of information theory, a novel scheme of unequal-interval frequency-hopping (FH) systems was proposed. For cases of spectrum overlapping systems and non-overlapping systems, the implementation methods were presented and the security performances were discussed theoretically. Firstly, the definitions of absolute and relative key amounts of FH systems, equal-interval and unequal-interval FH systems were given. Then, the absolute key amount and relative key amount were analyzed for equal-interval and unequal-interval FH systems. The results indicated that the absolute key amount had become the key point in improving the security and secrecy of FH systems, especially in today's epoch of highly developed computer science and IC design technology. Theoretical analysis and practical examples showed that the absolute key amount of unequal-interval FH systems was generally over two orders larger than that of equal-interval ones when spectrum overlapping was allowable. Therefore, there was great superiority in enhancing the security and secrecy for the scheme mentioned.
基金Guangxi project of improving Middle-aged/Young teachers'ability,Grant/Award Number:2020KY020323Fundamental Research Funds for the Central Universities,Grant/Award Number:CUC210A003。
文摘Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.
基金supported by the National Natural Science Foundation of China under Grant No.62062020 and No.72161005,NO.62002081,NO.62062017Technology Founda-tion of Guizhou Province(grant no.QianKeHeJiChu‐ZK[2022]‐General184)Guizhou Provincial Science and Technology Projects[2020]1Y265.
文摘Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep learning.Concretely,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually.It also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model training.The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.
基金This work was supported by the Education Department of Jiangsu Province (No. 04KJD520084 and 02KJD510019).
文摘We present an all-optical chaotic multi-quantum-well (MQW) laser repeater system to be used in long-haul chaotic communications. Chaotic synchronization is achieved among transmitter, repeater, and receiver. Chaotic repeater communications with a sinusoidal signal of 0.2-GHz modulation frequency and a digital signal of 0.4-Gb/s bit rate are numerically simulated, respectively. Calculation results illustrate that the signals are well decoded by the chaotic repeaters. Its bandwidth and the characteristics at much high bit rate are also analyzed. Simulation shows that the repeater can improve decoding quality, especially in higher bit rate chaotic communications.
