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

点击翻译按钮获取中文摘要

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