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公开(公告)号：US20140283091A1
公开(公告)日：2014-09-18
申请号：US13831948
申请日：2013-03-15
发明人： Li Zhang , Kunal Talwar , Aleksandar Nikolov
CPC分类号： G06F21/6245 , G06F17/18 , G06F17/30477 , G06F17/30554 , G06F21/60
摘要： The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ε-differential privacy (pure differential privacy) or is (ε,δ)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
摘要翻译： 对直方图的线性查询的隐私受到保护。 查询包含私有数据的数据库。 执行基本分解以递归地计算数据库空间的正交基准。 使用相关（或高斯）噪声和/或最小二乘估计，响应于查询生成并提供具有差分隐私的答案。 在一些实现中，差分隐私是“微分隐私”（纯差分隐私）或者是（＆egr;，δ） - 差异隐私（即近似差异隐私）。 在一些实现中，数据库中的数据可能是密集的。 这样的实现可以使用相关噪声而不使用最小二乘估计。 在其他实现中，数据库中的数据可能是稀疏的。 这样的实现可以使用或不使用相关噪声来进行最小二乘估计。
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公开(公告)号：US09672364B2
公开(公告)日：2017-06-06
申请号：US13831948
申请日：2013-03-15
发明人： Li Zhang , Kunal Talwar , Aleksandar Nikolov
CPC分类号： G06F21/6245 , G06F17/18 , G06F17/30477 , G06F17/30554 , G06F21/60
摘要： The privacy of linear queries on histograms is protected. A database containing private data is queried. Base decomposition is performed to recursively compute an orthonormal basis for the database space. Using correlated (or Gaussian) noise and/or least squares estimation, an answer having differential privacy is generated and provided in response to the query. In some implementations, the differential privacy is ε-differential privacy (pure differential privacy) or is (ε,δ)-differential privacy (i.e., approximate differential privacy). In some implementations, the data in the database may be dense. Such implementations may use correlated noise without using least squares estimation. In other implementations, the data in the database may be sparse. Such implementations may use least squares estimation with or without using correlated noise.
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