37970940
Prefix-preserving IP address anonymization: measurement-based security evaluation and a new cryptography-based scheme
Real-world traffic traces are crucial for Internet research, but only a very small percentage of traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that confidential and private information may be inferred from the trace. We focus on the problem of anonymizing IP addresses in a trace. More specifically, we are interested in prefix-preserving anonymization in which the prefix relationship among IP addresses is preserved in the anonymized trace, making such a trace usable in situations where prefix relationships are important. The goal of our work is two fold. First, we develop a cryptography-based, prefix-preserving anonymization technique that is provably as secure as the existing well-known TCPdpriv scheme, and unlike TCPdpriv, provides consistent prefix-preservation in large scale distributed setting. Second, we evaluate the security properties inherent in all prefix-preserving IP address anonymization schemes (including TCPdpriv). Through the analysis of Internet backbone traffic traces, we investigate the effect of some types of attacks on the security of any prefix-preserving anonymization algorithm. We also derive results for the optimum manner in which an attack should proceed, which provides a bound on the effectiveness of attacks in general.
12382521
On the design and performance of prefix-preserving IP traffic trace anonymization
Even though real-world Internet traffic traces are crucial for network research, only a tiny percentage of traffic traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that the confidential and private information may be inferred from the trace. In this paper we focus on the problem of anonymizing IP addresses in a trace. More specifically, we are interested in prefixpreserving anonymization in which the prefix relationship among IP addresses is preserved in the anonymized trace, making such a trace usable in situations where such prefix relationships are important. The goal of our work is two fold. First, we are interested in analyzing the security properties inherent in prefix-preserving IP address anonymization. Through the analysis of IP traffic traces, we investigate the effect of some types of attacks on the security of the prefix-preserving anonymization process. We also derive results for the optimum manner in which an attack should proceed which provides a bound on the performance of attacks in general. Second, we observe that an existing scheme used for prefix-preserving anonymization, TCPdpriv, has some drawbacks that limit its use in a large-scale, distributed setting. We develop an alternative cryptography-based, prefixpreserving anonymization technique to address these drawbacks while maintaining the same level of anonymity as TCPdpriv.
10379627
Medical video mining for efficient database indexing, management and access
To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
5651829
Utilizing Domain Knowledge in Neuroevolution
We propose a method called Rule-based ESP (RESP) for utilizing prior knowledge evolving Artificial Neural Networks (ANNs). First, KBANN-likete chniques are used to transform a set of rules into an ANN, then the ANN is trained using the Enforced Subpopulations (ESP) neuroevolution method. Empirical results in the Prey Capture domain show that RESP can reach higher level of performance than ESP. The results also suggest that incremental learning is not necessary with RESP, and it is often easier to design a set of rules than an incremental evolution scheme. In addition, an experiment with some of the rules deleted suggests that RESP is robust even with an incomplete knowledge base. RESP therefore provides a robust methodology for scaling up neuroevolution to harder tasks by utilizing existing knowledge about the domain.
1692629
Representing roles and purpose
Ontology designers often distinguish Entities (things that are) from Events (things that happen). It is not obvious how this division admits Roles (things that are, but only in the context of things that happen). For example, Person might be considered an Entity, while Employee is a Role. A Person remains a Person independent of the Events in which he participates. Someone is an Employee only by virtue of participating in an Employment Event. The problem of how to represent Roles is not new, but there is little consensus on a solution. In this paper, we present an ontology that finds a place for Roles as well as a representation that allows Roles to be related to Entities and Events to express the teleological notion of purpose.
10472812
Building concept ontology for medical video annotation
Most existing systems for content-based video retrieval (CBVR) are now amenable to support automatic low-level video content analysis and feature extraction, but they have limited effectiveness from a user's perspective. To support semantic video retrieval via keywords, we have proposed a novel framework by incorporating the concept ontology to enable more effective modeling and representation of semantic video concepts. Specifically, this novel framework includes: (a) Using the salient objects to achieve a middle-level understanding of the semantics of video contents; (b) Building a domain dependent concept ontology to enable multi-level modeling and representation of semantic video concepts; (c) Developing a multi-task boosting technique to achieve hierarchical video classifier training for automatic multi-level video annotation. The experimental results in a certain domain of medical education videos are also provided.
1153368
Indirect anaphora resolution as semantic path search
Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.