In 2005, Bao, et al. [Appl. Math. and Comput., vol.169, No.2, 2005] showed that Tzeng, et al.’s nonrepudiable threshold multi-proxy multi-signature scheme with shared verification was insecure, and proposed an improv...In 2005, Bao, et al. [Appl. Math. and Comput., vol.169, No.2, 2005] showed that Tzeng, et al.’s nonrepudiable threshold multi-proxy multi-signature scheme with shared verification was insecure, and proposed an improved scheme with no Share Distribution Center (SDC). This paper shows that Bao, et al.’s scheme suffers from the proxy relationship inversion attack and forgery attack, and pro- poses an improvement of Bao, et al.’s scheme.展开更多
This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitut...This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ε-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.展开更多
基金Supported by the National Natural Science Foundation of China (No.10671051)the Natural Science Foundation of Zhejiang Province (No.Y105067).
文摘In 2005, Bao, et al. [Appl. Math. and Comput., vol.169, No.2, 2005] showed that Tzeng, et al.’s nonrepudiable threshold multi-proxy multi-signature scheme with shared verification was insecure, and proposed an improved scheme with no Share Distribution Center (SDC). This paper shows that Bao, et al.’s scheme suffers from the proxy relationship inversion attack and forgery attack, and pro- poses an improvement of Bao, et al.’s scheme.
文摘This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ε-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.
