《动词：体貌与因果结构》（Verbs：Aspect and Causal Structure）是著名语言学家William Croft的最新力作,系统阐释了作者所创建的有关事件结构和论元实现的全新理论,是作者关于动词和句法近三十年研究的成果总结。该书2012年由牛津...《动词：体貌与因果结构》（Verbs：Aspect and Causal Structure）是著名语言学家William Croft的最新力作,系统阐释了作者所创建的有关事件结构和论元实现的全新理论,是作者关于动词和句法近三十年研究的成果总结。该书2012年由牛津大学出版社出版,共计448＋xvii页,分为十个章节,书末并附有术语表。展开更多
When querying databases containing sensitive information,the privacy of individuals stored in the database has to be guaranteed.Such guarantees are provided by differentially private mechanisms which add controlled no...When querying databases containing sensitive information,the privacy of individuals stored in the database has to be guaranteed.Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses.However,most such mechanisms do not take into consideration the valid range of the query being posed.Thus,noisy responses that fall outside of this range may potentially be produced.To rectify this and therefore improve the utility of the mechanism,the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized.However,such a data-dependent operation of normalization leaks additional information about the true query response,thereby violating the differential privacy guarantee.Here,we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution.We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations.We provide derivations of the optimal scaling parameter(i.e.,the minimal value that preserves differential privacy)for each class or provide an approximation thereof.As a result of this work,one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner.To demonstrate the benefits of our proposed method of normalization,we present an experimental comparison against other range-adherent mechanisms.We show that our proposed approach is able to provide improved utility over the alternative mechanisms.展开更多
文摘《动词：体貌与因果结构》（Verbs：Aspect and Causal Structure）是著名语言学家William Croft的最新力作,系统阐释了作者所创建的有关事件结构和论元实现的全新理论,是作者关于动词和句法近三十年研究的成果总结。该书2012年由牛津大学出版社出版,共计448＋xvii页,分为十个章节,书末并附有术语表。
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)under Grant Nos.RGPIN-2020-06482,RGPIN-2016-06253 and CGSD2-503941-2017.
文摘When querying databases containing sensitive information,the privacy of individuals stored in the database has to be guaranteed.Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses.However,most such mechanisms do not take into consideration the valid range of the query being posed.Thus,noisy responses that fall outside of this range may potentially be produced.To rectify this and therefore improve the utility of the mechanism,the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized.However,such a data-dependent operation of normalization leaks additional information about the true query response,thereby violating the differential privacy guarantee.Here,we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution.We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations.We provide derivations of the optimal scaling parameter(i.e.,the minimal value that preserves differential privacy)for each class or provide an approximation thereof.As a result of this work,one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner.To demonstrate the benefits of our proposed method of normalization,we present an experimental comparison against other range-adherent mechanisms.We show that our proposed approach is able to provide improved utility over the alternative mechanisms.
