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Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the most effective privacy protection mechanisms,differential privacy has been widely used in many fields.The existing histogram publishing methods for either static data set or dynamic data set mainly protect the privacy of sliding windows in data streams by adding unified noise.This leads to low data availability,high time complexity and weak privacy protection in their practical applications.In this paper,we tackle this problem by integrating the approximate counting techniques into the differential privacy and proposing an adaptive histogram publishing method for sliding window(APS).Firstly,the proposed APS predicates the distributional information of the sliding windows in the data stream by using an approximate counting method.Secondly,it computes an appropriate value suitable for publishing by checking the difference between estimated values and actual values.Finally,it reduces statistical errors within each interval by clustering.Theoretical analysis shows that the APS algorithm can effectively improve data availability and reduce running time while reducing the privacy budget.Experimental results on two different real data sets also verify the superiority of APS algorithm over existing grouping-based histogram publishing algorithms in terms of data availability and running time.

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Differential privacy is widely used as a privacy protection mechanism. Although there are many methods for publishing histograms on static data sets, there are fewer methods for publishing histograms based on sliding windows in a data stream environment,and they face the problem of high publishing errors of histograms. For this problem, this paper proposed a data stream differential privacy histogram publishing algorithm suitable for sliding window model HPA-SW. The algorithm first divided a sliding window into k sub-blocks based on the idea of data partitioning, and used this parameter to control and adjusted the statistical error of the data histogram. Then, the algorithm compared the data distribution of two adjacent histograms to optimize the privacy budget allocation of the current window, so as to quickly calculate the local optimal histogram. In order to verify the effectiveness of the proposed algorithm, this paper firstly proved through rigorous theoretical derivation that the designed algorithm met the requirements of differential privacy, and its approximation error did not exceed W/2k. Secondly, through experimental comparison on real data sets, it shows that the publishing error of the proposed algorithm is low, which 50% lower than SSHP algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to trade-off between the privacy and accuracy of published histogram, this paper proposed a histogram publishing algorithm based on sampling sorting and hierarchical partitioning. To gather the similar data together, it introduced the exponential mechanism into the roulette sampling technology, which was used to sort the original histogram by sampling. And in order to reduce the approximation error caused by the different data lied in a data group, this paper used a hierarchical partitioning method to partition the sorted histogram. Finally, it added the Laplace noise into the grouped histogram, and restored the original histogram order to get the published histogram. The simulation results show that the algorithm can effectively improve the accuracy of published histograms on the premise of satisfying differential privacy. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Local volatility model is widely used in the fields of risk management, options pricing, etc. The model can not only describe the "smile" and the term structure of volatility, but can also ensure the completeness of the market. The core goal of local volatility research is to calculate the implied volatility. In this paper, the implied volatility surfaces arc built with both parametric and non-parametric methods to obtain smoother volatility surfaces. Parametric method can ensure the absence of arbitrage. Meanwhile, a closed form of solving the local volatility model with non-parametric method is proposed, which can eliminate the approximation error. In addition, the pricing of Shanghai 50ETF option in China based on the local volatility model is studied by comparing effects of the models from aspects of in-the-sample, out-of-the sample and hedging pricing errors. The empirical results show that the non-parametric method is better than the parametric method for errors in-the-samplc; the parametric method has better results for out-of-sample and hedging effects. Particularly, both the options pricing and hedging effects of the implied volatility modeling methods are better than nn-modeled method in the aspects that the pricing errors can be decreased by more than half and the mean square error can be reduced by 1~2 orders of magnitude. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on motion tracking control algorithm, the effects of two typical estimation errors, i. e. offset error and proportional error, of CM sideslip angle estimation on vehicle stability control is analyzed by simulation. The results show that for ensuring light driver's operation intensity and high stability of vehicle, the offset error of CM sideslip angle should not exceed 2 degrees and its proportional error should not exceed 20% of its real value. Providing there is no much effects on vehicle stability, reasonably underestimated CM sideslip angle can improve the maneuverability of vehicles. [ABSTRACT FROM AUTHOR]