公开(公告)号：US09064364B2

公开(公告)日：2015-06-23

申请号：US10690778

申请日：2003-10-22

申请人： Naoki Abe ,  Carl E. Abrams ,  Chidanand V. Apte ,  Bishwaranjan Bhattacharjee ,  Kenneth A. Goldman ,  Matthias Gruetzner ,  Matthew A. Hilbert ,  John Langford ,  Sriram K. Padmanabhan ,  Charles P. Tresser ,  Kathleen M. Troidle ,  Philip S. Yu

发明人： Naoki Abe ,  Carl E. Abrams ,  Chidanand V. Apte ,  Bishwaranjan Bhattacharjee ,  Kenneth A. Goldman ,  Matthias Gruetzner ,  Matthew A. Hilbert ,  John Langford ,  Sriram K. Padmanabhan ,  Charles P. Tresser ,  Kathleen M. Troidle ,  Philip S. Yu

IPC分类号： G06Q20/02 ,  G07F7/10 ,  G06F21/62 ,  G06F21/72 ,  G06Q20/34 ,  G07F19/00

CPC分类号： G06Q20/4016 ,  G06F21/121 ,  G06F21/602 ,  G06F21/6218 ,  G06F21/6245 ,  G06F21/72 ,  G06F2221/07 ,  G06F2221/2101 ,  G06Q20/341 ,  G06Q20/3829 ,  G06Q2220/00 ,  G07F7/1008 ,  G07F7/1083 ,  G07F19/207 ,  H04L63/04

摘要： Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

摘要翻译： 用于维护欺诈检测系统内的数据和操作的安全性和机密性的各种实施例。 这些实施例中的每一个都利用安全架构，其中：（1）对数据的访问仅限于批准或授权的实体; （2）接收到的数据的机密细节可以很容易地识别和隐藏; 和（3）可以识别和暴露出非机密的机密细节。

公开(公告)号：US08229191B2

公开(公告)日：2012-07-24

申请号：US12042961

申请日：2008-03-05

申请人： Michail Vlachos ,  Philip S. Yu

发明人： Michail Vlachos ,  Philip S. Yu

IPC分类号： G06K9/00 ,  G06K9/36

CPC分类号： G06T1/0071 ,  G06T2201/0051 ,  G06T2201/0052 ,  G06T2201/0061 ,  G06T2201/0083

摘要： Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

摘要翻译： 提供了用于将诸如个人患者信息之类的元数据嵌入到从患者获得的实际医疗数据信号中的系统和方法，其中在给定医疗数据信号中嵌入两个水印，鲁棒水印和脆弱水印。 鲁棒水印包括使用离散傅里叶变换和附加嵌入结合到医疗数据信号的频域中的元数据的二进制编码表示。 错误纠正码也可以使用汉明编码加到元数据的二进制表示中。 给定的鲁棒水印可以被并入多次在医疗数据信号中。 在修改后的医疗信号的空间域中包含鲁棒水印的经修改的医学信号之上添加脆弱水印。 脆弱水印利用散列函数产生通过医疗数据信号并入的随机序列。

公开(公告)号：US08117331B2

公开(公告)日：2012-02-14

申请号：US12165524

申请日：2008-06-30

申请人： Bugra Gedik ,  Kun-Lung Wu ,  Philip S. Yu

发明人： Bugra Gedik ,  Kun-Lung Wu ,  Philip S. Yu

IPC分类号： G06F15/16

CPC分类号： H04L49/90

摘要： One embodiment of the present method and apparatus adaptive load shedding includes receiving at least one data stream (comprising a plurality of tuples, or data items) into a first sliding window of memory. A subset of tuples from the received data stream is then selected for processing in accordance with at least one data stream operation, such as a data stream join operation. Tuples that are not selected for processing are ignored. The number of tuples selected and the specific tuples selected depend at least in part on a variety of dynamic parameters, including the rate at which the data stream (and any other processed data streams) is received, time delays associated with the received data stream, a direction of a join operation performed on the data stream and the values of the individual tuples with respect to an expected output.

摘要翻译： 本发明的方法和设备的一个实施例是自适应负载脱落包括将至少一个数据流（包括多个元组或数据项）接收到存储器的第一滑动窗口中。 然后根据至少一个数据流操作（例如数据流加入操作）选择来自接收到的数据流的元组的子集用于处理。 未选择处理的元组将被忽略。 所选择的元组的数量和所选择的特定元组至少部分取决于各种动态参数，包括接收数据流（和任何其他处理的数据流）的速率，与接收到的数据流相关联的时间延迟， 对数据流执行的连接操作的方向和相对于预期输出的单个元组的值。

公开(公告)号：US07904471B2

公开(公告)日：2011-03-08

申请号：US11836171

申请日：2007-08-09

申请人： Charu Aggarwal ,  Philip S. Yu

发明人： Charu Aggarwal ,  Philip S. Yu

IPC分类号： G06F17/30

CPC分类号： H04L63/0407 ,  G06F21/6254 ,  Y10S707/99942 ,  Y10S707/99943

摘要： Privacy in data mining of sparse high dimensional data records is preserved by transforming the data records into anonymized data records. This transformation involves creating a sketch-based private representation of each data record, each data record containing only a small number of non-zero attribute value in relation to the high dimensionality of the data records.

摘要翻译： 通过将数据记录转换为匿名数据记录来保留稀疏高维数据记录的数据挖掘隐私。 该变换涉及创建每个数据记录的基于草图的私有表示，每个数据记录仅包含相对于数据记录的高维数的少量非零属性值。

公开(公告)号：US07870398B2

公开(公告)日：2011-01-11

申请号：US11626847

申请日：2007-01-25

申请人： Chang-shing Perng ,  Haixun Wang ,  Jian Yin ,  Philip S. Yu

发明人： Chang-shing Perng ,  Haixun Wang ,  Jian Yin ,  Philip S. Yu

IPC分类号： G06F12/14 ,  G06F7/00

CPC分类号： G06F21/64 ,  G06F17/30286 ,  G06F21/6245 ,  G06F2221/2115

摘要： A method, system and computer program product for confirming the validity of data returned from a data store. A data store contains a primary data set encrypted using a first encryption and a secondary data set using a second encryption. The secondary data set is a subset of the primary data set. A client issues a substantive query against the data store to retrieve a primary data result belonging to the primary data set. A query interface issues at least one validating query against the data store. Each validating query returns a secondary data result belonging to the secondary data set. The query interface receives the secondary data result and provides a data invalid notification if data satisfying the substantive query included in an unencrypted form of the secondary data result is not contained in an unencrypted form of the primary data result.

摘要翻译： 一种用于确认从数据存储返回的数据的有效性的方法，系统和计算机程序产品。 数据存储包含使用第一加密加密的主数据集和使用第二加密的辅数据集。 辅助数据集是主数据集的子集。 客户端对数据存储器发出实质性查询以检索属于主数据集的主数据结果。 查询界面对数据存储区发出至少一个验证查询。 每个验证查询返回属于辅助数据集的辅助数据结果。 如果满足辅助数据结果的未加密形式的实质性查询的数据未包含在主数据结果的未加密形式中，则查询接口接收辅助数据结果并提供数据无效通知。

公开(公告)号：US07849138B2

公开(公告)日：2010-12-07

申请号：US12038386

申请日：2008-02-27

申请人： Xiaohui Gu ,  Zon-Yin Shae ,  Zhen Wen ,  Philip S. Yu

发明人： Xiaohui Gu ,  Zon-Yin Shae ,  Zhen Wen ,  Philip S. Yu

IPC分类号： G06F15/16 ,  H04L12/16

CPC分类号： H04L12/1818 ,  H04L45/16 ,  H04L45/48

摘要： A system and computer program product for establishing multi-party VoIP conference audio calls in a distributed, peer-to-peer network where any number of nodes are able to arbitrarily and asynchronously start or stop producing audio output to be mixed into a single composite audio stream that is distributed to all nodes. A single distribution tree is used that has optimal communications characteristics to distribute the composite audio signal to all nodes. An audio mixing tree is established and maintained by adaptively and dynamically adding and merging intermediate mixing nodes operating between user nodes and the root of the single distribution tree. The intermediate mixing nodes and the root of the single distribution tree are all hosted, in an exemplary embodiment, on user nodes that are endpoints of the distribution tree.

摘要翻译： 一种用于在分布式对等网络中建立多方VoIP会议音频呼叫的系统和计算机程序产品，其中任何数量的节点能够任意地和异步地开始或停止产生要混合到单个复合音频中的音频输出 分发给所有节点的流。 使用具有最佳通信特性以将复合音频信号分配给所有节点的单个分发树。 通过自适应地动态地添加和合并在用户节点和单个分发树的根之间运行的中间混合节点来建立和维护音频混合树。 在示例性实施例中，分发树的中间混合节点和根分别在作为分发树的端点的用户节点上托管。

公开(公告)号：US07822730B2

公开(公告)日：2010-10-26

申请号：US11931129

申请日：2007-10-31

申请人： Wei Fan ,  Haixun Wang ,  Philip S. Yu

发明人： Wei Fan ,  Haixun Wang ,  Philip S. Yu

IPC分类号： G06F17/30

CPC分类号： G06N99/005 ,  Y10S707/99931

摘要： Most recent research of scalable inductive learning on very large streaming dataset focuses on eliminating memory constraints and reducing the number of sequential data scans. However, state-of-the-art algorithms still require multiple scans over the data set and use sophisticated control mechanisms and data structures. There is discussed herein a general inductive learning framework that scans the dataset exactly once. Then, there is proposed an extension based on Hoeffding's inequality that scans the dataset less than once. The proposed frameworks are applicable to a wide range of inductive learners.

摘要翻译： 对最大流式数据集的可伸缩归纳学习的最新研究着重于消除记忆限制并减少顺序数据扫描的次数。 然而，最先进的算法仍然需要对数据集进行多次扫描，并使用复杂的控制机制和数据结构。 这里讨论了一般的归纳学习框架，该框架一次扫描数据集。 然后，提出了一种基于Hoeffding不等式的扩展，可以扫描数据集不止一次。 提出的框架适用于广泛的归纳学习者。

公开(公告)号：US20090226056A1

公开(公告)日：2009-09-10

申请号：US12042961

申请日：2008-03-05

申请人： Michail Vlachos ,  Philip S. Yu

发明人： Michail Vlachos ,  Philip S. Yu

IPC分类号： G06K9/00

CPC分类号： G06T1/0071 ,  G06T2201/0051 ,  G06T2201/0052 ,  G06T2201/0061 ,  G06T2201/0083

摘要： Systems and methods for embedding metadata such as personal patient information within actual medical data signals obtained from a patient are provided wherein two watermarks, a robust watermark and a fragile watermark are embedded in a given medical data signal. The robust watermark includes a binary coded representation of the metadata that is incorporated into the frequency domain of the medical data signal using discrete Fourier transformations and additive embedding. Error correcting code can also be added to the binary representation of the metadata using Hamming coding. A given robust watermark can be incorporated multiple times in the medical data signal. The fragile watermark is added on top of the modified medical signal containing the robust watermark in the spatial domain of the modified medical signal. The fragile watermark utilizes hash function to generate random sequences that are incorporated through the medical data signal.

摘要翻译： 提供了用于将诸如个人患者信息之类的元数据嵌入到从患者获得的实际医疗数据信号中的系统和方法，其中在给定医疗数据信号中嵌入两个水印，鲁棒水印和脆弱水印。 鲁棒水印包括使用离散傅里叶变换和附加嵌入将并入到医疗数据信号的频域中的元数据的二进制编码表示。 错误纠正码也可以使用汉明编码加到元数据的二进制表示中。 给定的鲁棒水印可以被并入多次在医疗数据信号中。 在修改后的医疗信号的空间域中包含鲁棒水印的经修改的医学信号之上添加脆弱水印。 脆弱水印利用散列函数产生通过医疗数据信号并入的随机序列。

公开(公告)号：US20090187914A1

公开(公告)日：2009-07-23

申请号：US12372568

申请日：2009-02-17

申请人： Yun Chi ,  Haixun Wang ,  Philip S. Yu

发明人： Yun Chi ,  Haixun Wang ,  Philip S. Yu

IPC分类号： G06F9/46 ,  G06N5/02

CPC分类号： G06K9/6297 ,  H04L43/028

摘要： Load shedding schemes for mining data streams. A scoring function is used to rank the importance of stream elements, and those elements with high importance are investigated. In the context of not knowing the exact feature values of a data stream, the use of a Markov model is proposed herein for predicting the feature distribution of a data stream. Based on the predicted feature distribution, one can make classification decisions to maximize the expected benefits. In addition, there is proposed herein the employment of a quality of decision (QoD) metric to measure the level of uncertainty in decisions and to guide load shedding. A load shedding scheme such as presented herein assigns available resources to multiple data streams to maximize the quality of classification decisions. Furthermore, such a load shedding scheme is able to learn and adapt to changing data characteristics in the data streams.

摘要翻译： 挖掘数据流的加载脱落方案。 使用评分函数对流元素的重要性进行排序，并调查那些具有重要意义的元素。 在不知道数据流的精确特征值的上下文中，本文提出了使用马尔可夫模型来预测数据流的特征分布。 基于预测的特征分布，可以进行分类决定，以最大限度地提高预期效益。 此外，在此提出采用质量决策（QoD）度量来衡量决策中的不确定性水平并指导负荷脱落。 诸如此处呈现的负载脱落方案将可用资源分配给多个数据流以最大化分类决定的质量。 此外，这种负载脱落方案能够学习和适应数据流中不断变化的数据特性。

公开(公告)号：US07505876B2

公开(公告)日：2009-03-17

申请号：US11620679

申请日：2007-01-07

申请人： Spyridon Papadimitriou ,  Jimeng Sun ,  Philip S. Yu

发明人： Spyridon Papadimitriou ,  Jimeng Sun ,  Philip S. Yu

IPC分类号： G06F15/00 ,  G06F17/30

CPC分类号： G06F17/30516 ,  G06F17/30241 ,  G06F17/30489 ,  G06F17/30551 ,  Y10S707/99936

摘要： In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.

摘要翻译： 在一个示例性实施例中，本发明的一些主要方面如下：（i）数据模型：我们引入张量流以处理多方面流的大集合; 和（ii）算法框架：我们提出基于窗口的张量分析（WTA）来有效地从张量流中提取核心模式。 张量表示与在线分析处理（OLAP）中的数据立方体相关。 然而，我们的本发明专注于为每个窗口构造简单的摘要，而不仅仅是组织数据以沿着每个方面或方面的组合来产生简单的聚合。