The research on sensitizer for dye sensitized solar cells based on (metallo)porphyrin/phthalocyanine materials were reviewed, and experimental design and assembly method were advised. Latest progress and research stat...The research on sensitizer for dye sensitized solar cells based on (metallo)porphyrin/phthalocyanine materials were reviewed, and experimental design and assembly method were advised. Latest progress and research status of sensitizer dyes based on metalloporphyrins applied in dye sensitized solar cells was summarized. The preparation and construction of sensitizer electrodes and dye sensitized solar cells based on metal organic frameworks (MOFs) of (metallo)porphyrin/phthalocyanine were projected.展开更多
混洗差分隐私(SDP)模型能兼顾用户端的隐私保护程度和服务器端发布结果的可用性,更适用于隐私保护的大数据收集和统计发布场景。针对目前SDP频率估计方法的洗牌效率较低和混洗过程安全性不足等问题,进行以下工作:首先,设计基于优化椭圆...混洗差分隐私(SDP)模型能兼顾用户端的隐私保护程度和服务器端发布结果的可用性,更适用于隐私保护的大数据收集和统计发布场景。针对目前SDP频率估计方法的洗牌效率较低和混洗过程安全性不足等问题,进行以下工作:首先,设计基于优化椭圆曲线的混洗差分隐私盲签名算法(SDPBSA),以实现对篡改或伪造信息的鉴别,提高混洗过程的安全性;其次,提出矩阵列重排转置(MCRT)洗牌方法,以利用随机的矩阵列重排和矩阵转置操作实现数据混洗,提高混洗过程的效率;最后,结合上述方法构建完整的SDP频率估计隐私保护框架——SM-SDP(SDP based on blind Signature and Matrix column rearrangement transposition),并通过理论分析讨论它的隐私性和误差级别。在Normal、Zipf和IPUMS(Integrated Public Use Microdata Series)等数据集上的实验结果表明,相较于Fisher-Yates、ORShuffle(Oblivious Recursive Shuffling)和MRS(Message Random Shuffling)等洗牌方法,MCRT洗牌方法的洗牌效率提升了1~2个数量级;相较于mixDUMP、PSDP(Personalized Differential Privacy in Shuffle model)和HP-SDP(Histogram Publication with SDP)等频率估计方法,SM-SDP框架在不同比例恶意数据存在时的均方误差(MSE)降低了2~11个数量级。展开更多
文摘The research on sensitizer for dye sensitized solar cells based on (metallo)porphyrin/phthalocyanine materials were reviewed, and experimental design and assembly method were advised. Latest progress and research status of sensitizer dyes based on metalloporphyrins applied in dye sensitized solar cells was summarized. The preparation and construction of sensitizer electrodes and dye sensitized solar cells based on metal organic frameworks (MOFs) of (metallo)porphyrin/phthalocyanine were projected.
文摘混洗差分隐私(SDP)模型能兼顾用户端的隐私保护程度和服务器端发布结果的可用性,更适用于隐私保护的大数据收集和统计发布场景。针对目前SDP频率估计方法的洗牌效率较低和混洗过程安全性不足等问题,进行以下工作:首先,设计基于优化椭圆曲线的混洗差分隐私盲签名算法(SDPBSA),以实现对篡改或伪造信息的鉴别,提高混洗过程的安全性;其次,提出矩阵列重排转置(MCRT)洗牌方法,以利用随机的矩阵列重排和矩阵转置操作实现数据混洗,提高混洗过程的效率;最后,结合上述方法构建完整的SDP频率估计隐私保护框架——SM-SDP(SDP based on blind Signature and Matrix column rearrangement transposition),并通过理论分析讨论它的隐私性和误差级别。在Normal、Zipf和IPUMS(Integrated Public Use Microdata Series)等数据集上的实验结果表明,相较于Fisher-Yates、ORShuffle(Oblivious Recursive Shuffling)和MRS(Message Random Shuffling)等洗牌方法,MCRT洗牌方法的洗牌效率提升了1~2个数量级;相较于mixDUMP、PSDP(Personalized Differential Privacy in Shuffle model)和HP-SDP(Histogram Publication with SDP)等频率估计方法,SM-SDP框架在不同比例恶意数据存在时的均方误差(MSE)降低了2~11个数量级。
