Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks
Abstract
:1. Introduction
- (1)
- The historical check-in sequence. The check-in sequence is constituted by the user’s check-in locations sorted by the timestamps. Obviously, the check-in sequences reflect the user’s behavior. For example, from the historical check-in records, the number of check-in sequences including the positions lj and lj+1 both is 10. Then, we find that the user checks-in at lm 3 times between lj and lj+1. Thus, we guess that the user will check-in to the position lm from the location lj to the location lj+1 with the probability 0.3(=3/10) in the future.
- (2)
- Personalized POIs preferences. In LBSN, each POI is labeled with several service categories. Generally, users’ preferences for the service categories are different. For example, nightclub enthusiasts prefer to visit different bars, while travel enthusiasts like to visit different tourist attractions. Therefore, the personalized preference of the service category can also be used to infer the probability of the user visiting the hidden location lm.
- (3)
- Social networks. Generally, the behaviors of friends are similar. Specifically, users often wander around various streets with their friends and go to a good restaurant or a shopping mall, etc. A user is more likely go to a place recommended by his/her friends. Suppose that a user u’s friend u* always checks-in to the lm. Then, although u does not check-in at lm during the movement from lj to lj+1, the attackers can also infer the likelihood that u visited lm based on the user similarities between u and u*.
- (4)
- Geographical location. In general, the geographical proximity of POIs has a significant impact on the user’s check-in behavior. The probability of accessing li+1 after checking-in at li depends on the distance between the two POIs. For example, users usually go to a mall or a movie theater nearby for convenience. On the other hand, the reachability of a position can be used to prune a sensitive hidden location. Specifically, if a user takes a short traveling time that is less than the minimum time required between the two locations li and lj+1, then the user u certainly cannot visit lm. That is, the location lm is not reachable.
- (5)
- POIs Popularity. If a POI is prevalent, then the POI is more attractive to a user. That means, the visiting probability of the users to this POI will be high. Therefore, we can use the popularity of POIs to infer the accessing probability as well.
2. Literature Review
3. Background
4. Hidden Location Inference Attack Models and Algorithm
4.1. WBI: Weighted Bayesian Hidden Location Inference Model
4.2. Hlpi: Hidden Location Inference Model Based on Multi-Factor Fusion
4.3. Hidden Location Inference Attack Algoriyhm
- (1)
- The sensitive category set SSu which the user u regards to be sensitive is saved in the authorization server. The authorization server is trusted. When the user u wants to use the check-in services, u can send the check-in request with pre-check-in location lj+1 at time tj+1 to the authorization server.
- (2)
- When the authorization server receives users’ check-in requests, Algorithm 1 is utilized. The hidden locations between the user’s previous check-in location lj and pre-check-in location lj+1 are computed. The inferred hidden POIs are sorted by the computed visiting probabilities. The authorization server will send a privacy warning message to u when the categories of the hidden POIs fall into the sensitive category set SSu. The most probable POIs whose category is sensitive are pushed to u in the warning message. The warning message will ask whether the use still wants to check-in at POI lj+1 at time tj+1.
- (3)
- The users can make a choice by themselves. If the user still wants to check-in at location lj, the authorization server will forward the check-in request to the LBS server. Otherwise, the authorization server will drop this check-in request, meaning the check-in service is sacrificed while the user’s privacy is protected.
Algorithm 1. Hidden Location Inference Algorithm. |
Input: lj, lj+1, W, S, C, P, G=<V, E> Output: Pair set of hidden location and the probability <Lm, Pm>
|
5. Results
5.1. Setting
5.2. Results and Analysis
5.2.1. Accuracy
5.2.2. Effectiveness
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Dataset | Foursquare | Yelp |
---|---|---|
Number of users | 717,382 | 70,817 |
Number of point of interests (POIs) | 49,027 | 15,579 |
Number of POI Category | 602 | 591 |
Number of check-in | 206,416 | 335,022 |
Number of friend pairs | 2,767,235 | 303,032 |
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Pan, X.; Chen, W.; Wu, L. Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks. Mathematics 2020, 8, 262. https://doi.org/10.3390/math8020262
Pan X, Chen W, Wu L. Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks. Mathematics. 2020; 8(2):262. https://doi.org/10.3390/math8020262
Chicago/Turabian StylePan, Xiao, Weizhang Chen, and Lei Wu. 2020. "Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks" Mathematics 8, no. 2: 262. https://doi.org/10.3390/math8020262
APA StylePan, X., Chen, W., & Wu, L. (2020). Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks. Mathematics, 8(2), 262. https://doi.org/10.3390/math8020262