# Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes
## 3 Results
### Identifying User's Identity--Privacy Threats in Geolocation Apps/Services
The authors conclude that students may bid lower than the average population due to a lack of spouses, fewer responsibilities, or their environment. This study enables reaching conclusions on how individuals value their geolocation information by providing a representative amount of money for disclosing them. This representation could also be used in estimating the importance of protecting users' personal information. Mobile devices that incorporate a variety of sensors have opened a new discussion about mobile crowd sensing (MCS). However useful these incentive mechanisms may be, they have proven to be quite costly for individual workers in terms of time and system resources. Therefore, it is important to provide the necessary incentive mechanisms to boost users' participation. Those incentives are primarily of economic origin for compensation reasons. The authors in [25] designed the INCEPTION, which is \"a novel MCS system framework with an integrated design of the incentive, data aggregation, and data perturbation mechanism\". In other words, it proposes a weighted data aggregation mechanism that takes into consideration users' reliability while choosing reliable workers who tend to provide reliable data so as to generate highly accurate aggregated results. The cost of the compensation for sensing and privacy leakage satisfies both truthfulness and individual rationality while minimizing the platform's total payment for worker recruiting with a guaranteed approximation ratio. Furthermore, INCEPTION guarantees maintaining users' privacy and the accuracy of the outcomes. INCEPTION was validated using both theoretical analysis and a variety of simulations. Another paper that combines the utilization of MCS systems while protecting users' privacy is [26]. This paper sheds light upon a private incentive mechanism that protects user's privacy (worker's bid information), while at the same time aims to incentivize worker participation in MCS systems. More precisely, the author proposes a differentially private incentive mechanism that minimizes the total sum of the payment using an approximation ratio. The proposed platform collects the bids for each set of tasks, and then serves as an auctioneer in order to determine the winner while preserving the privacy of each worker against their co-workers. Except for the theoretical analysis, the paper includes an adequate number of simulations. [PERSON] et al. [27] also focus on designing auction-based incentive mechanisms while examining k-anonymity location privacy. As they observe the rise of location-based services (LBSs), such as Foursquare, Google Latitude, and Where, they discuss LBSs as being both informational and entertainment oriented in providing services that handle a user's geographical position. Simultaneously, they observe that users tend not to be concerned about their location privacy. Thus, the authors need to provide incentives for mobile users to participate in anonymity sets in order to achieve k-anonymity. In this paper, they examine different cases. They initiate their analysis and contribution by considering the case where all users have the same privacy degree requirement. After that, they examine the case in which they have different requirements. Last but not least, the third case refers to a more intriguing case where mobile users can disclose both valuations and requirements. The authors designed auction-based incentive mechanisms for each of the aforementioned cases while following computational efficiency, individual rationality, budget balance, and truthfulness as necessary critical properties that should be met in an auction.