Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles
Abstract
:1. Introduction
- (1)
- We explore the literature on pseudonym-based anonymity assurance for messaging in the IoVs.
- (2)
- Next, we propose a solution to estimate the next state of vehicles and their speed and direction before sending the pseudonym-changing alert.
- (3)
- We also deal with the exchange of pseudonyms to reduce costs and ensure anonymity as well.
- (4)
- Finally, simulations are performed to validate the results where the proposed scheme outperforms in contrast to three dominating schemes.
2. Literature Review
2.1. Mix-Context-Based Schemes
2.2. Mix-Zone-Based Schemes
3. System Model and Problem Statement
- (1)
- The TA is used to allocate pseudonyms to vehicles when they enter the network. In case a vehicle is conducting suspicious activities in the network, after receiving the report from the RSU, the TA revokes the pseudonym of that vehicle. So, the main purpose of this entity is to allocate, revoke and keep the link between former and new pseudonyms.
- (2)
- Vehicles are the basic components of the system model, which is equipped with the OBU, GPS and sensors. The vehicles can communicate with each other and the RSU for sharing safety beacons, and share pseudonym information and other information. During traveling on roads, vehicles need to know accurate information about their destination.
- (3)
- The location-based server provides the following facilities: i) inquiring about vehicle appeal to the RSU, ii) sends a request to the location-based server (LBS) for providing accurate location information for moving to the desired destination.
- (4)
- The RSU monitors traffic and informs vehicles about it in a timely manner. In this case, the pseudonyms are insufficient, and the RSU requests the TA to provide more numbers. In the case of malicious nodes in the network, the RSU instructs the TA to revoke its pseudonym. The system model of the proposed scheme is shown in Figure 2.
Adversary Model
4. Efficient Pseudonym Consumption Protocol
Algorithm 1: Efficient Pseudonym Consumption Algorithm |
//When intended vehicle v get BSM 1. N_position = BSM.pos (); 2. Neigh_dis = dis(my_position, N_position) 3. If (Neigh_dis ≤ T) then 4. Neigh_v++ 5. store ← store + Neigh_v; 6. Else drop BSM. 7. End if //intended vehicle v aims to disseminate BSM in upcoming timeslot 8. while (OBU status is active) do 9. wait (beacon interval) 10. Ready (BSM); 11. if (nodes ≥ k) then 12. vehicles_trails ← kalman_filter(store); 13. for i ← 1 to Neigh_v do 14. if (Euclidean (vehicles_trails(i).pos, current_state.pos) ≤ Close_R) then 15. adjacent ← adjacent + vehicles_trails(i); 16. End if 17. End for 18. if (!adjacent.empty()) then 19. Call Function Neighbor_speed ← BSM.speed() 20. if (Neighbor_speed < thresholdmin) OR (Neighbor_speed > thresholdmax) then 21. Call Function BSM (Delay) 22. Else 23. N_direction = Call Function BSM_direction () 24. if (std:: equal(mine_direction, N_direction)) then 25. if (Neigh_v ≥ threshold &&((Neigh_v (Readyflag) && v_readyflag == 1)) then 26. Call Function Update cooperatively pseudonym () 27. Set Readyflag_bit to 0 28. elseif (Neigh_v < threshold && ((Neigh_v (Readyflag) && v_readyflag == 1)) 29. Random exchange of unused pseudonym (Vi, Vj) 30. Set Readyflag_bit to 0 31. End if 32. End if 33. End if 34. If (adjacent.empty()) then 35. Locality ← False //no vehicle is in transmission range of vehicle v 36. End if 37. If (v_pseudolife > stable_span) then 38. Call Function Update pseudonym (); 39. Set Readyflag_bit to 0 40. End if 41.End if 42.End while |
5. Results and Discussion
5.1. Average Percentage of Adversary Attains Traceability
5.2. Average Percentage of Adversary Attains Normalized Traceability
5.3. Pseudonym Consumption
5.4. BSM Loss Rate
5.5. Average Confusion for Attacker Due to Change in Pseudonym
5.6. Proportion of Vehicles That Changed Pseudonym
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sr. | Notation | Description |
---|---|---|
1. | Number of neighbors | |
2. | Neigh_dis | Neighbor distance |
3. | Neigh_v | Vehicles in locality of vehicle v |
4. | thresholdmin | Minimum threshold speed |
5. | threshold | Neighbor threshold value |
6. | Vi | Vehicle v |
7. | Vj | Neighboring vehicles |
8. | thresholdmax | Maximum threshold speed |
9. | Close_R | Close range |
10. | N_direction | Direction of neighbor vehicles |
Parameters | Values |
---|---|
Simulation time | 300 s |
Number of vehicles | 50, 100, 150, 200 |
Transmission range | 300 m |
Pseudonym stable time | 50 s |
Minimum speed threshold | 5 m/s |
Maximum speed threshold | 30 m/s |
Close range | 100 m |
Neighbor threshold | 40 |
Operating system | Ubuntu 16.04 |
Coupling protocol | TraCi |
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Mushtaq, M.; Ullah, A.; Ashraf, H.; Jhanjhi, N.Z.; Masud, M.; Alqhatani, A.; Alnfiai, M.M. Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles. Sensors 2023, 23, 5217. https://doi.org/10.3390/s23115217
Mushtaq M, Ullah A, Ashraf H, Jhanjhi NZ, Masud M, Alqhatani A, Alnfiai MM. Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles. Sensors. 2023; 23(11):5217. https://doi.org/10.3390/s23115217
Chicago/Turabian StyleMushtaq, Mehreen, Ata Ullah, Humaira Ashraf, N.Z Jhanjhi, Mehedi Masud, Abdulmajeed Alqhatani, and Mrim M. Alnfiai. 2023. "Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles" Sensors 23, no. 11: 5217. https://doi.org/10.3390/s23115217