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Ubiquitous computing blended with context awareness gives user the facility of “anywhere anytime” computing. Location based services represents a class of context aware computing. Involvement of location as the primary input in location based services triggered concerns for user’s privacy. Most of the privacy work in domain of location based services relies on obfuscation strategy along with K anonymity. The proposed work acknowledges the idea of calculating value of K for K anonymity using context factors in fuzzy format. However, with increasing number of these fuzzy context factors resulting in more fuzzy rules, the system will tend to get slower. In order to address this issue, requirement is to reduce the size of rule base without hampering the performance much. Goal of the proposed work is to attain scalability and high performance for the above said system. Towards this, reduction of number of rules in the rule base, of fuzzy inference system has been done using Fuzzy C Means and Genetic Algorithm. Results of reduced rule base have been compared with the results of exhaustive rule base. It has been identified that number of rules can be reduced up to considerable extent with comparable performances and acceptable level of error.

Soft Computing for Scalability in Context Aware Location based Services