Widespread adoption and usage of Location based services (LBS) has eventually raised the concern for user’s privacy. Various privacy preserving techniques are based on the idea of forwarding the cloaking area instead of actual location to service provider. For these scenarios, results of the query request will be based on geometric/nearest distance between query issuer and service requested. Such techniques do not include real time context which is very important in determining security, accessibility etc of the service and enhancing the service quality. In this work a novel method has been proposed which takes into account the real time context for determining the results of query. Real time context has been obtained through crowd resources present in close proximity (cloaking area). A fuzzy inference system has been implemented which takes These real time context parameters along with the time of the day and nearest distance are supplied to a Fuzzy inference system (which is a part of middleware) as input and generates a new rank for the service/request. The original contribution of the paper is the introduction of a comprehensive framework consisting of a real time context-aware component which is collected through crowd sourcing. The proposed method is evaluated by taking user feedback about their satisfaction. User feedback for all such systems is compared using Kruskal Wallis test for significant differences. It has been discovered that user satisfaction for the proposed system stochastically dominates the other similar systems available.