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    • 3. 发明授权
    • Private clustering and statistical queries while analyzing a large database
    • 在分析大型数据库时进行私有聚类和统计查询
    • US07676454B2
    • 2010-03-09
    • US11069116
    • 2005-03-01
    • Cynthia DworkFrank David McSherryYaacov Nissim KoblinerAvrim L. Blum
    • Cynthia DworkFrank David McSherryYaacov Nissim KoblinerAvrim L. Blum
    • G06F7/00G06F17/30
    • G06F17/30539G06F21/6227G06F21/6245Y10S707/99933
    • A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query is received from a querying entity query and is passed to the database, and an answer is received in response. An amount of noise is generated and added to the answer to result in an obscured answer, and the obscured answer is returned to the querying entity. The noise is normally distributed around zero with a particular variance. The variance R may be determined in accordance with R>8 T log2(T/δ)/ε2, where T is the permitted number of queries T, δ is the utter failure probability, and ε is the largest admissible increase in confidence. Thus, a level of protection of privacy is provided to each individual represented within the database. Example noise generation techniques, systems, and methods may be used for privacy preservation in such areas as k means, principal component analysis, statistical query learning models, and perceptron algorithms.
    • 数据库具有多个条目和对每个条目共同的多个属性,其中每个条目对应于个人。 从查询实体查询中接收到查询,并将其传递给数据库,并接收答复。 产生噪声量并将其加到答案中,导致模糊的答案,并将隐藏的答案返回给查询实体。 噪声通常分布在零附近,具有特定的差异。 方差R可以根据R> 8 T log2(T /δ)/&egr; 2确定,其中T是允许的查询数T,δ是全失效概率,&egr; 是信心最大的允许增加。 因此,对数据库中表示的每个个体提供了一个隐私保护级别。 噪声生成技术,系统和方法的示例可以用于k意味着,主成分分析,统计查询学习模型和感知器算法等领域的隐私保护。
    • 4. 发明申请
    • Private clustering and statistical queries while analyzing a large database
    • 在分析大型数据库时进行私有聚类和统计查询
    • US20060200431A1
    • 2006-09-07
    • US11069116
    • 2005-03-01
    • Cynthia DworkFrank McSherryYaacov Nissim KoblinerAvrim Blum
    • Cynthia DworkFrank McSherryYaacov Nissim KoblinerAvrim Blum
    • G06F15/18
    • G06F17/30539G06F21/6227G06F21/6245Y10S707/99933
    • A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query is received from a querying entity query and is passed to the database, and an answer is received in response. An amount of noise is generated and added to the answer to result in an obscured answer, and the obscured answer is returned to the querying entity. The noise is normally distributed around zero with a particular variance. The variance R may be determined in accordance with R>8 T log2(T/δ)/ε2, where T is the permitted number of queries T, δ is the utter failure probability, and ε is the largest admissible increase in confidence. Thus, a level of protection of privacy is provided to each individual represented within the database. Example noise generation techniques, systems, and methods may be used for privacy preservation in such areas as k means, principal component analysis, statistical query learning models, and perceptron algorithms.
    • 数据库具有多个条目和对每个条目共同的多个属性,其中每个条目对应于个人。 从查询实体查询中接收到查询,并将其传递给数据库,并接收答复。 产生噪声量并将其加到答案中,导致模糊的答案,并将隐藏的答案返回给查询实体。 噪声通常分布在零附近,具有特定的差异。 方差R可以根据R> 8 T log 2(T / delta)/ε2> 2确定,其中T是允许的查询数T,delta是 完全失败概率和ε是置信度最大的允许增加。 因此,对数据库中表示的每个个体提供了一个隐私保护级别。 噪声生成技术,系统和方法的示例可以用于k意味着,主成分分析,统计查询学习模型和感知器算法等领域的隐私保护。