An Efficient Location Privacy Protection Scheme Based on the Chinese Remainder Theorem

Traditional k-anonymity schemes cannot protect a user’s privacy perfectly in big data and mobile network environments. In fact, existing k-anonymity schemes only protect location in datasets with small granularity. But in larger granularity datasets, a user’s geographical region-location is always exposed in realizations of k-anonymity because of interaction with neighboring nodes. And if a user could not find enough adjacent access points, most existing schemes would be invalid. How to protect location information has become an important issue. But it has not attracted much attention. To solve this problem, two location-privacy protection models are proposed. Then a new generalized k-anonymity Location Privacy Protection Scheme based on the Chinese Remainder Theorem (LPSS-CRT) in Location-Based Services (LBSs) is proposed. We prove that it can guarantee that users can access LBSs without leaking their region-location information, which means the scheme can achieve perfect anonymity. Analysis shows that LPPS-CRT is more secure in protecting location privacy, including region information, and is more efficient, than similar schemes. It is suitable for dynamic environments for different users’ privacy protection requests.