Albelaihy, AbdullahCazalas, JonathanThayananthan, Vijey2022-07-012022-07-012018-06-15Albelaihy, A., Cazalas, J., & Thayananthan, V. (2018). Privacy-preserving queries for LBS: Independent secured hash function. Journal of Theoretical and Applied Information Technology, 96(11), 3578-3588. http://www.jatit.org/volumes/Vol96No11/29Vol96No11.pdfhttp://www.jatit.org/volumes/Vol96No11/29Vol96No11.pdfhttp://hdl.handle.net/11416/660While location-based services have become ubiquitous, seemingly permeating our personal and professional lives, their inherent nature poses security risks to users, who are forced to reveal their highly-sensitive location data in order to make effective use of the service. Towards this end, a litany of techniques have been proposed to provide efficient answers for privacy-preserving queries in LBS. Spatial bloom filters were initially proposed as an efficient data structure used to manage special and geographic information in an space-efficient manner. Unfortunately, bloom filters suffer from two deficiencies: they leak at most one bit of information per query, and the hash functions require careful design and security analysis in order to be orthogonal and independent. In fact, developing quality hash function is paramount. We propose a method to automatically generate good, independent hash functions, with the goal of reducing information leakage. This means that even if one of the hash function is broken, for any reason, nothing can be learned about any other hash function. The results show that our proposed Hash functions are less dependent and leaked than the compared approach, while still seeing a notable improvement in performance.en-USData privacyLocation-based servicesHash functionArticle