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Protection of database security via ...

Chen, Yu.

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Protection of database security via collaborative inference detection.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Protection of database security via collaborative inference detection./
作者:	Chen, Yu.
面頁冊數:	109 p.
附註:	Adviser: Wesley W. Chu.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3299591
ISBN:	9780549444541

Protection of database security via collaborative inference detection.
Chen, Yu.

Protection of database security via collaborative inference detection. - 109 p.

Adviser: Wesley W. Chu.

Thesis (Ph.D.)--University of California, Los Angeles, 2007.

Malicious users can infer sensitive information from a series of seemingly innocuous data access. To protect the sensitive data content, we proposed a probabilistic inference approach to treat the query-time inference detection problem.

ISBN: 9780549444541Subjects--Topical Terms:

626642
Computer Science.

Protection of database security via collaborative inference detection.
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245 1 0 $a Protection of database security via collaborative inference detection.
300 $a 109 p.
500 $a Adviser: Wesley W. Chu.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0408.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2007.
520 $a Malicious users can infer sensitive information from a series of seemingly innocuous data access. To protect the sensitive data content, we proposed a probabilistic inference approach to treat the query-time inference detection problem.
520 $a Based on data dependency, database schema and semantic knowledge, we construct a semantic inference model (SIM) that represents the possible inference channels from any attribute to the pre-assigned security attributes. To reduce inference computation complexity, the instantiated SIM can he mapped into a Bayesian network. Thus, we can use available Bayesian network tools (e.g. Samlam) to evaluate the inference probability along the inference channels. For a single user, when the user poses a query, the detection system will examine his/her past query log and calculate the probability of inferring the security information. The query request will be denied if the inference probability exceeds the pre-specified threshold.
520 $a For multi-user, the users may collaborate with their query answers to increase the probability of inferring sensitive information. Therefore, we develop a model to evaluate collaborative inference based on the query sequences of collaborators and their task-sensitive collaboration levels. Experimental studies reveal that information authoritativeness, communication fidelity and honesty in collaboration are three key factors that affect the level of collaboration effectiveness.
520 $a Sensitivity analysis of attributes in the Bayesian network can be used to study the sensitivity of the inference channels. Our study reveals that the nodes closer to the security node have stronger inference effect on the security node. Thus sensitivity analysis of these close nodes can assist domain experts to specify the threshold of the security node to ensure its robustness.
520 $a In summary, we develop a technique that prevents users from inferring sensitive information from a series of seemingly innocuous queries. The contribution of this research consists of (1) Derive probabilistic data dependency, relational database schema and domain-specific semantic knowledge and represent them as probabilistic inference channels in a Semantic Inference Model. (2) Map the instantiated Semantic Inference Model into a Bayesian network for efficient and scalable inference computation. (3) Propose an inference detection framework for multiple collaborative users and study the collaboration level between collaborators.
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