The k anonymity was one of the first algorithms applied for privacy protection in location-based service(LBS).The k anonymity exhibits its disadvantages gradually, such as being easily attacked by continuous queries attacking algorithm, the larger k value for higher security level lead to more pointless cost of bandwidth and load of LBS server. This article analyzes the causes of the problems, and proposes a new idea based on clustering algorithm to improve the k anonymity algorithm.
Currently, many universities have set up their own system for providing employment information service, problems that following the independently operating and maintaining modes are: on the one hand, employers have to register a number of university employment information system to obtain information on a number of graduates; on the other hand, graduates also need to browse a number of university employment information site to get more information on business recruitment, which severely reduces the transparency and efficiency of information data queries. To solve the above problems, this paper propose the university employment information integration model on the basis of distributed storage and computing technology, which realizes the integration of multiple heterogeneous employment information database.
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