TC: Small: Collaborative: Protocols for Privacy-Preserving Scalable Record Matching and Ontology Alignment

TC：小型：协作：隐私保护可扩展记录匹配和本体对齐协议

• 批准号: 1016722
• 负责人: Elisa Bertino
• 金额: $ 24万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016722&HistoricalAwards=false
• 关键词: TC; Small; Collaborative; Protocols; Privacy

Many application domains, such as intelligence, counter-terrorism,forensics, disease control, often need to cross-match multiple verylarge datasets, such as watch lists. Because those datasets may contain privacy-sensitive or confidential information, the use of efficientprivacy-preserving protocols for cross-matching different datasets iscrucial. The problem of privacy-preserving record matching has beenaddressed by the use of Secure Multi-party Computation (SMC) protocols.Under these protocols, the data are converted to series of functionswith private inputs. However a major drawback of SMC-based protocolsis that they involve extensive cryptographic primitives such ashomomorphic encryption which do not scale to the size of practicalproblems. As a result, SMC-based protocols cannot be used for resourceconstrained data-intensive privacy-preserving record matching approaches directly. This project develops a novel approach based on the observationthat to apply SMC to practical applications, one needs to bridge the gap between the size of the datasets that can efficiently be matched using SMC protocols and the size of the datasets seen in practice. The approach taken by the project tackles the problem from a novel angle by developing techniques to reduce the size of practical problems by employing privacy-preserving data sanitization methods. The project thus solves the privacy-preserving data matching problems through the followingsteps. First, to protect the privacy of data subjects, useful statistics about data is gathered using differential privacy. Second, differentially private statistics are shared among the parties involved in data matching. These parties then identify potential matching pairs where fruitful matching may occur. Such a step is referred to as data blocking. Finally, SMC techniques are applied to these candidates to accurately cross-matchinformation. In addition to syntactic matching, semantic matching is supported by which records are compared according to some semantic similarity functions.The semantic matching protocols includes techniques for matching andaligning ontologies, as the use of ontologies is crucial for an effectivesemantic matching. This project is the first to use differential privacy forefficient privacy-preserving record matching that also leverages semantics-basedapproach and a privacy-preserving approach to ontology alignment. The techniquesdeveloped in the project are the first to achieve efficient privacy-preservingmatching of large scale data sets using differential privacy, thus overcoming the scalability problems of conventional SMC techniques. The approach developed in this project expands the opportunities and contexts for data use by enabling the cross-match of multiple data archives, possibly owned by different parties, without violating the privacy of the data. Many applications, of interest for our society, will benefit by such opportunities.For further information see the project web site at the URL:http://www.cs.purdue.edu/homes/bertino/prirelink

许多应用领域，例如智能，反恐，取证，疾病控制，通常需要交叉匹配多个非常大的数据集，例如观察列表。由于这些数据集可能包含对隐私敏感或机密信息，因此使用有效的保留协议来交叉匹配不同的数据集很重要。使用安全的多方计算（SMC）协议已解决了保护隐私记录匹配的问题。根据这些协议，数据将转换为私有输入的一系列函数。然而，基于SMC的协议的主要缺点，它们涉及广泛的加密原始图，例如ashomormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormormortives，其规模不超过实用问题的大小。结果，基于SMC的协议不能直接用于资源密集的数据密集型隐私记录匹配方法。该项目基于观察到将SMC应用于实际应用的新方法，需要弥合数据集大小之间的差距，这些差距可以使用SMC协议和实践中看到的数据集的大小有效匹配。该项目采用的方法通过开发技术来通过采用隐私数据消毒方法来减少实际问题的大小来解决问题。因此，该项目通过以下步骤解决了保护隐私的数据匹配问题。首先，为了保护数据主体的隐私，使用差异隐私收集了有关数据的有用统计信息。其次，与数据匹配的各方之间共享了差异性私人统计信息。然后，这些政党确定可能发生富有成果的匹配的潜在匹配对。这样的步骤称为数据阻止。最后，将SMC技术应用于这些候选者，以准确地进行交叉匹配。除句法匹配外，还支持根据某些语义相似性函数比较记录的语义匹配。语义匹配协议包括用于匹配和安装本体论的技术，因为对本体的使用对于有效的效果匹配至关重要。该项目是第一个使用差异隐私保护隐私的记录匹配的项目，该匹配还利用基于语义的应用程序和一种保护隐私的方法来进行本体学对齐。该项目中开发的技术是第一个使用差异隐私对大规模数据集实现有效的隐私性保护的技术，从而克服了常规SMC技术的可伸缩性问题。该项目中开发的方法通过启用可能由不同各方拥有的多个数据档案的交叉匹配而不违反数据隐私而扩大数据使用的机会和上下文。许多对我们社会感兴趣的应用程序将受益于此类机会。有关更多信息，请参阅网站上的项目网站：http：//www.cs.purdue.edu/homes/bertino/bertino/pririrink