Regrettably, existing privacy-protecting approaches for LBS have a lot of restrictions, for example needing a completely-reliable 3rd party, offering limited privacy guarantees and incurring high communication overhead. Location-based services2 (LBS) require customers to continuously report their whereabouts to some potentially untrusted server to acquire services according to their whereabouts, which could expose these to privacy risks. Within this paper, we advise a person-defined privacy grid system known as dynamic grid system (DGS) the very first holistic system that satisfies four essential needs for privacy-protecting snapshot and continuous LBS. (1) The machine only needs a semi-reliable 3rd party, accountable for transporting out simple matching procedures properly. This semi-reliable 3rd party doesn't have any details about a user’s location. (2) Secure snapshot and continuous location privacy is guaranteed under our defined foe models. (3) Experimental results reveal that our DGS is much more efficient compared to condition-of-the-art privacy-protecting way of continuous LBS. The communication cost for that user doesn't rely on the user’s preferred privacy level, it just is dependent on the amount of relevant sights near the consumer. (4) Although we simply concentrate on range and k-nearest-neighbor queries within this work, our bodies can be simply extended to aid other spatial queries without altering the calculations operated by the semi-reliable 3rd party and also the database server, provided the needed search section of a spatial query could be abstracted into spatial regions.