K-anonymity Location Privacy Research Articles

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

Retracted: Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection

Retracted: Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection

Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection

The spatial crowdsourcing task places workers at a risk of privacy leakage. If positional information is not required to submit, it will result in an increased error rate and number of spammers, which together affects the quality of spatial crowdsourcing. In this paper, a spatial crowdsourcing quality control model is proposed, called SCQCM. In the model, the spatial k-anonymity algorithm is used to protect the position privacy of the general spatial crowdsourcing workers. Next, an ELM (extreme learning machine) algorithm is used to detect spammers, while an EM (expectation maximization) algorithm is used to estimate the error rate. Finally, different parameters are selected, and the efficiency of the model is simulated. The results showed that the spatial crowdsourcing model proposed in this paper guaranteed the quality of crowdsourcing projects on the premise of protecting the privacy of workers.

K-Anonymity Location Privacy Algorithm Based on Clustering

The accuracy of user location information is inversely proportional to the user’s privacy preserving degree $k$ , and is proportional to quality of query service. In order to balance the conflict between privacy preserving security and query quality caused by the accuracy of location information, a clustering algorithm aiming at eliminating outliers based on the k-anonymity location privacy preserving model is proposed, which is used to realize the establishment of anonymous group in the anonymous model. The distribution of user in the anonymous group is optimized. The idea of replacing the user location query by the center of the anonymous group is proposed. The number of repeated queries is reduced, and the quality of query service is improved on the premise of ensuring security through the experimental analysis and comparison with other schemes.

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.

Improvement of K-Anonymity Location Privacy Protection Algorithm Based on Hierarchy Clustering

The k anonymity was one of the first algorithms applied for privacy protection in location-based service(LBS).The k anonymity exhibits its disadvantages gradually, such as being easily attacked by continuous queries attacking algorithm, the larger k value for higher security level lead to more pointless cost of bandwidth and load of LBS server. This article analyzes the causes of the problems, and proposes a new idea based on clustering algorithm to improve the k anonymity algorithm.