Privacy-Preserving Real-Time Navigation System Using Vehicular Crowdsourcing

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1 Privacy-Preserving Real-Time Navigation System Using Vehicular Crowdsourcing (Invited Paper) Jianbing Ni, Xiaodong Lin, Kuan Zhang, and Xuemin (Sherman) Shen Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, {j25ni, k52zhang, Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Ontario, Canada L1H 7K4, Abstract Traffic congestions cause not only the timeconsuming and frustrating experiences to drivers, but also other critical problems, such as fuel waste, air pollution and accidents. Real-time traffic information exchange can avoid vehicles being congested on roads. However, when the drivers are acquiring the traffic information, their privacy is inevitable to be disclosed. To preserve the driver s privacy, in this paper, we propose a privacypreserving real-time navigation system (PRIN) using vehicular crowdsourcing. In PRIN, the RSUs cooperatively find an optimal path for the querying vehicle to the destination according to the real-time traffic information crowdsourced by the vehicles in their coverage areas. The querying vehicle retrieves the navigation result from each RSU successively when entering its coverage area, and follows the proper driving route to the next RSU, until reaching its destination. During these querying, crowdsourcing and retrieving processes, the driver s personal information, such as location, identity, is protected from being disclosed to attackers. In addition, a trusted authority can trace the drivers identities if they upload false traffic information. Finally, we discuss the properties of conditional privacy preservation and demonstrate the efficiency of PRIN. I. INTRODUCTION Traffic congestions, especially in urban areas, have negative social and economic impact on the society, leading to economic cost of lost hours and delayed appointments. In the ten most-congested cities in USA, drivers spent around 42 hours during traffic jams in 2014, wasting about $121 billion in fuel [1]. Traffic jams are not only frustrating for drivers, but also a major source of air pollution, causing more than 5 million premature deaths in 2015 worldwide [2]. Real-time traffic information plays an essential role to monitor congestion and predict the optimal driving paths for drivers. Currently, traffic-aware automotive navigation systems and traffic-based navigation mobile applications [3] can provide drivers with up-to-date routing information to avoid road congestions. Both of them need the traffic information to generate the optimal driving routes. Since the traffic information on roads change with time, how to collect the real-time road conditions and find the proper paths for drivers promptly is an essential problem in navigation systems. Recently, vehicular ad hoc networks (VANETs) become increasingly popular in both industry and academia [4]. The vehicles, installed with onboard unit (OBU) devices [5], are able to communicate with other nearby vehicles, (i.e., vehicleto-vehicle (V2V) communications), and with roadside units (RSUs), (i.e., vehicle-to-roadside (V2R) communications). Having V2V and V2R communications, a variety of promising applications have been developed to improve safety and enrich driving experience for drivers. VANET-based navigation systems [6], [7] emerge to provide real-time navigation services for drivers on roads. By means of the widely deployed vehicular communication infrastructure, the vehicles do not need any extra device but the OBUs to enjoy the navigation services. Specifically, a vehicle on road can query the driving route to the destination to the nearby RSUs, and the RSUs cooperatively find the fastest route for the driver by utilizing the traffic information. Finally, the querying vehicle follows the route to reach the destination. The navigation system has the advantage that the RSUs collect, analyze the traffic information locally utilizing vehicular crowdsourcing and return the navigation routes to drivers with low latency. However, security and privacy [8], [9], [10], [11] are primary concerns for drivers in VANET-based navigation systems. There have been numerous controversies due to the track exposure [12]. For instance, Google and Apple have built real-time navigation applications for drivers based on up-to-date traffic information, but collected drivers location and destination in return [3], which may reveal sensitive information about the drivers personal lives. In VANET-based navigation systems, the vehicles frequently communicate with the RSUs to query the driving routes, retrieve the navigation results on behalf of a querier, or provide traffic information to help other vehicles to find proper paths. Since the vehicle s location is tightly related to the driver, an attacker can learn the travelling route of the vehicle and predict the location of the driver at a specific time, or even identify some personal information about the driver, such as habits, references, religious belief, health condition, political affiliations, according to the visiting frequency of some particular places. Therefore, it is necessary to preserve the drivers privacy for the wide acceptance of VANET-based navigation services. Several VANET-based vehicle navigation systems [13], [14], [15] have been proposed to achieve drivers privacy preservation. Chim et al. [13] proposed a secure and privacy-preserving navigation system, in which RSUs use traffic information

2 to guide drivers to their destinations in a distributed way. Nevertheless, this system is vulnerable to insider attacks since a master key is shared among all vehicles. Cho et al. [14] introduced a security-enhanced navigation system to eliminate the master key distribution and simplify the anonymous credential acquisition. However, they does not investigate the approach to collecting traffic information securely and distributively. Sur et al. [15] proposed a secure navigation system based on vehicular cloud from trapdoor hash function and zero-knowledge proof. The anonymous credentials in this system can be only used once. In this paper, we propose a Privacy-preserving Real-tIme Navigation system (PRIN) using vehicular crowdsourcing to collect real-time traffic information and prevent the privacy disclosure for drivers. The main contributions of this paper are three folds. We propose PRIN to achieve the traffic-aware navigation for drivers by utilizing vehicular crowdsourcing. With the traffic information crowdsourced from vehicles, the RSUs can collaboratively find a proper driving route to the destination for the querying driver. Finally, the driver reaches the destination following the proper path with shorter time and lower fuel consumption. PRIN preserves the drivers privacy by utilizing the randomization technique. Specifically, a vehicle sends the navigation query to a nearby RSU anonymously. The vehicles who participate in the crowdsourcing tasks submit the traffic information to the RSUs without exposing their real identities through the randomizable signature [16]. Meanwhile, a trusted authority can trace the vehicle s identity if the vehicle uploads forged traffic information. We discuss the security properties of conditional privacy preservation in PRIN and demonstrate the efficiency of PRIN in terms of computational and communication overhead. The remainder of this paper is organized as follows. We introduce system model, threat model and security goals in section II. In section III, we propose the PRIN system, followed by security discussion in section IV, and performance evaluation in section V, respectively. We conclude our paper in section VI. II. PROBLEM STATEMENT In this section, we state the problem by formalizing system and threat models, and identify security goals. A. System Model The real-time navigation system consists of a trusted authority (TA), a cloud, vehicles and RSUs. The responsibilities of TA are to issue anonymous credentials and trace the vehicle identity. The cloud provides centralized navigation service for drivers, e.g., Google Map. Each vehicle is equipped with an irreplaceable and temper-proof OBU device, which enables to perform some simple computations, communicate with other vehicles and RSUs, and has a small amount of readonly memory. RSUs communicate with each other and with vehicles that are driving through. Each RSU has rich resource, Fig. 1. System Model. in the sense that it has enough storage space to maintain traffic information and computational capacity to compute proper paths for drivers. Fig. 1 shows the system model of real-time navigation system. Firstly, each vehicle applies to the TA for an anonymous credential. Then, a vehicle using the navigation service, acts as a querier to send a navigation query to the nearby RSU, including the current location, desired destination and expired time. The RSU forwards this query to the last RSU that covers the destination through the relaying RSUs hop by hop. Upon receiving the navigation query, each RSU sends a crowdsourcing task to the vehicles in its coverage area, and the vehicles who perform the task report the location, speed and traffic information to the RSU. After that, RSUs find a proper driving route for the querying vehicle in its area according to the map of coverage area and the traffic information received from vehicles. The RSUs also analyze and further forward the traffic information to cloud for supporting cloud-based navigation service. Finally, the querying vehicle successively retrieves the navigation results from the RSUs when they enter the coverage regions of RSUs until it reaches the destination. B. Threat Model The security threats come from external and internal attackers. An external attacker may compromise RSUs to learn some personal information about drivers. An eavesdropper listens on the wireless communication channels to capture the transmitting messages and strive to obtain drivers privacy. Internal attackers, e.g., the honest-but-curious RSUs, may learn the position of a specific driver, and further obtain some sensitive information, e.g., habits, references, religious belief, political affiliation, by analyzing the forwarding messages, including navigation queries and results. The vehicles may try to obtain the navigation results queried by other vehicles to enjoy free navigation services if they happen to have the same destinations. In addition, some vehicles may report the forged traffic information to enjoy a pleasant and uncongested driving experience. C. Security Goals We aim to construct a real-time navigation system with the following security goals.

3 Querier Privacy. The identity of the querying driver should be preserved against the attackers. Although the vehicle s location and destination would be exposed to RSUs, the attacker cannot link a navigation query to a specific vehicle. Moreover, no attacker should be able to identify whether two navigation queries are sent by the same vehicle. Driver Privacy. The identity of a driver who performs the crowdsourcing task should not be disclosed to others. In this way, the attackers are unable to identify the source of a specific crowdsourcing response and distinguish whether two responses are uploaded by the same vehicle. Traceability. The TA can trace the real identities of the querying vehicles who sent the parking navigation queries or the vehicles that participated in the crowdsourcing tasks released by RSUs in the event of a dispute occurred. III. THE PRIN SYSTEM In this section, we propose the overview of PRIN system and its construction in details. The PRIN consists of five phases: system initialization, navigation querying, vehicular crowdsourcing, result retrieving and identity tracing. A. Overview of PRIN Since the transmission of a vehicle s navigation query follows the shortest path from the first RSU who receives the query from the vehicle to the last RSU that covers the desired destination, the querying vehicle will successively go through the coverage areas of these RSUs. Inspired by the optimality principle [17], we divide the driving route from the source to the destination into several segments based on the coverage areas of the relaying RSUs as shown in Fig. 2. Since each segment is optimal substructure [17], the optimal sub-route in each coverage area of RSU can constitute an optimal policy of the global path from the source to the destination. Therefore, we can successively find the optimal route for the querying vehicle in each coverage area of the passing RSU until the querying vehicle reaches the destination. Nevertheless, the shortest path in each coverage area may not be the optimal route due to the traffic congestion and car accidents. Hence, we utilize the vehicular crowdsourcing to collect real-time traffic information and thereby find a proper route in the coverage area of each RSU on the path from source to destination. Finally, the querying vehicle successively retrieves the navigation result from each RSU when entering its coverage area. This framework is superior to existing solutions [13] that mainly depend on the assumption that a moving vehicle has to obtain the results from the first RSU, which is quite challenging in reality, when the vehicle moves at a pretty high speed. B. System Initialization Let G 1, G 2 and G T be three cyclic groups of the same large prime order p. A bilinear map ê : G 1 G 2 G T is defined as type 3 pairing, in which G 1 G 2 and there is no efficiently computable homomorphism between G 1 and G 2 in either direction. g is a generator of G 1 with g 1 G1, and ĝ Fig. 2. Example of Roads from Source to Destination. is a generator of G 2 with ĝ 1 G2. Let H : {0, 1} Z p be a cryptographic hash function. The TA randomly chooses (x 0, x 1 ) Z 2 p as its secret key and computes ( X 0, X 1 ) = (ĝ x0, ĝ x1 ) as its public key. Each RSU has a unique identifier R to identify its location and a map of its coverage area to mark the traffic condition. R also maintains a routing table to determine where data packets should be directed. R randomly chooses y Z p as its secret key and computes Y = g y as its public key. Each vehicle has a unique identifier V, which can be the vehicle licence. The vehicle randomly picks (v, v 1 ) Z 2 p to compute (V, V 1, V 2 ) (g v, X 1 v v1 X 0, ). ĝv1 and sends (V, V, V 1, V 2 ) to the TA, along with the zeroknowledge proof: PK = {(v, v 1 ) : V = g v V 1 = X 1 v v1 X 0 V 2 = ĝ v1 }. The TA firstly computes V = V x0 1 / V 2. Then, the TA verifies the validity of PK and checks the equation ê(v, X? 1 ) = ê(g, V ). If either is invalid, the TA returns failure and aborts. Otherwise, the TA randomly picks u Z p to calculate (A 1, A 2, A 3 ) (g u, (g x0 V x1 ) u, ê(a 1, X 1 )). Finally, the TA sends (A 1, A 2, A 3 ) to V through a secure channel, and stores (V, V, V ) in a secure database. V sets its secret key as skv = (v, A 1, A 2, A 3 ) and the corresponding public key as pkv = V, and stores skv in the read-only memory of the OBU. C. Navigation Querying When a vehicle V with (skv, pkv ) drives on the road, it can send a navigation query to the nearby RSU, denoted as R 1. Specifically, the querier utilizes the OBU to generate the basic navigation information, including current location CL, desired destination DEST, current time t 1, expired time t 2, etc., and performs the following steps to generate a navigation query: Randomly choose w Z p to generate a temporary public key U = ĝ w and pick a random N Z p as the sequence number of the query. Utilize Y 1 to encrypt (U, CL, DEST ) by randomly choosing r 1 Z p, r 2 G 1 and computing C 1 = g r1, C 2 = r 2 Y r1 1, C 3 = AES ENC (r 2, U CL DEST ). Randomize (A 1, A 2, A 3) by selecting two random (k 1, k 2) Z 2 p to calculate (B 1, B 2, B 3) ((A 1) k 1, (A 2 ) k 1, (A 3 ) k 1 k 2 ), c = H(B 1, B 2, B 3, N, t 1, t 2, U, CL, DEST ), τ = k 2 + c v, and output (B 1, B 2, c, τ ).

4 Finally, the querier V stores (U, w) on the OBU and sends the navigation query Q = (N, t 1, t 2, C 1, C 2, C 3, B1, B2, c, τ ) to R 1, when V is in the coverage area of R 1. Upon receiving the navigation query Q, R 1 firstly decrypts r 2 = C 2 C y1 1 and recovers U CL DEST = AES DEC (r 2, C 3 ). Then, R 1 verifies the validity of the signature (B1, B2, c, τ ) by computing B = ê(b1, c X 0 )ê(b2, ĝ c )ê(b1, X τ 1 ) and checking whether c =? H(B1, B2, B, N, t 1, t 2, U, CL, DEST ). If not, R 1 returns failure and aborts; otherwise, it checks the routing table to find the next RSU R 2 according to the destination DEST. Then, R 1 randomly picks r 1 Z p, r 2 G 1 to compute C 1 = g r 1, C 2 = r 2Y r 1 2, C 3 = AES ENC (r 2, U CL DEST ). Finally, R 1 forwards the query Q = (N, t 1, t 2, C 1, C 2, C 3, B1, B2, c, τ ) to R 2. When having the navigation query Q, R 2 performs the same operations as R 1, and checks the routing table to forward the query to the next RSU until the navigation query reaches the last RSU R n, which covers the destination of the querying vehicle V. R n checks the validity of the signature (B1, B2, c, τ ) and obtains (N, t 1, t 2, U, CL, DEST ). D. Vehicular Crowdsourcing After receiving the navigation query, all RSUs {R 1,, R n } try to find an optimal path in their coverage areas by utilizing crowdsourcing individually. Every RSU R j {R 1,, R n } releases a crowdsourcing task of traffic information collection to all the vehicles in its coverage area. If a vehicle V i with (skv i, pkv i ) decides to perform this task. It firstly generates a traffic information report P i, which includes the current location, time, driving speed and road conditions (e.g., hazard, accidents, traffic jam, road closure). Then, V i randomizes (A 1i, A 2i, A 3i ) by choosing two random values (k 1i, k 2i ) Z 2 p to compute (B 1i, B 2i, B 3i ) (A k1i 1i, Ak1i 2i, Ak1ik2i 3i ). V i calculates c i = H(N, P i, B 1i, B 2i, B 3i ), τ i = k 2i +c i v i and outputs (B 1i, B 2i, c i, τ i ). Finally, V i returns the crowdsourcing response R i = (N, P i, B 1i, B 2i, c i, τ i ) to the R j. Upon receiving R i, R j verifies the signature by computing ci B i = ê(b 1i, X 0 )ê(b 2i, ĝ ci τi )ê(b 1i, X 1 )? and checking whether c i = H(N, Pi, B 1i, B 2i, B i ). If not, R j returns failure; otherwise, R j keeps R i on its database temporarily. Then, according to the real-time traffic information collected through vehicular crowdsourcing, R j generates the weight of every edge on the map of coverage area as shown in Fig. 3. Each weight denotes the time cost for a vehicle driving from an intersection to the nearby intersections. After that, R j utilizes the Dijkstra s algorithm [18] to find the optimal path SP j in its coverage area. Finally, R j randomly picks r 1i Z p, r 2i G 2 to compute (e 1i, e 2i, e 3i ) = (ĝ r1i, r 2i U r1i, AES ENC (r 2i, SP j )), K j = U yj and keeps the navigation result (N, K j, e 1i, e 2i, e 3i ). In addition, R j uploads the traffic information report P i to the cloud for supporting cloud-based navigation service. Fig. 3. E. Result Retrieving Example of Finding Optimal Path. When the querying vehicle V enters the coverage area of R j, it reads (U, w) on the OBU and calculates Kj = Y j w. Then, V randomly chooses (u 1, u 2) Z 2 p to compute ( B 1, B 2, B 3) ((A 1) u 1, (A 2 ) u 1, (A 3 ) u 1 u 2 ). V calculates ĉ = H(t 3, Kj, B 1, B 2, B 3), ˆτ = u 2 + ĉ v, and outputs ( B 1, B 2, ĉ, ˆτ ), where t 3 is the current time used to prevent replay attacks. Finally, V sends the retrieving query RQ = (t 3, Kj, B 1, B 2, ĉ, ˆτ ) to R j. Upon receiving RQ from V, R j computes B = ê( B 1, Xĉ 0 )ê( B 2, ĝ ĉ )ê( B 1, ˆτ X 1 ) and checks whether ĉ? = H(t3, Kj, B 1, B 2, B ). If not, R j returns failure and aborts. Otherwise, R j finds the corresponding navigation result (N, K j, e 1i, e 2i, e 3i ) based on Kj. R j randomly picks r j Z p to calculate σ 1j = g rj, σ 2j = H(N, R j, e 1i, e 2i, e 3i, σ 1j ) and σ 3j = r j + y j σ 2j, and returns the result NR j = (N, R j, e 1i, e 2i, e 3i, σ 1j, σ 3j ) to V. Upon obtaining NR j, V verifies the signature by computing σ 4j = H(N, R j, e 1i, e 2i, e 3i, σ 1j ) and checking whether? = g σ3j. If not, V aborts and returns failure; 1i, SP j = σ 1j Y σ4j j otherwise, it recovers SP j as r 2i = e 2i e w AES DEC (r 2i, e 3i ). According to SP j, V follows the optimal path to enter the coverage area of R j+1, and then retrieves the navigation result from R j+1 until reaching the destination. F. Identity Tracing The TA can use the vehicle s signature (B 1, B 2, c, τ) to trace the identity of the driver. It checks whether ê(b 2, ĝ)? = ê(b 1, X 0 )ê(b 1, V ) holds or not, until it gets a match. IV. SECURITY DISCUSSION In this section, we demonstrate the achievement of security and privacy goals mentioned in II-C for our PRIN. Querier Privacy. We utilize the Elgamal scheme and the AES scheme to encrypt the vehicle s location and destination in each hop from the vehicle to the last RSU. Thus, only the RSUs that the vehicle drives through can obtain the source and the destination. To prevent the RSUs from linking the navigation query or the retrieving query to a specific vehicle, V randomizes the credential (A 1, A 2, A 3) as (B 1, B 2, c, τ ), which is a group signature [16]. Therefore, if the underlying group signature can guarantee the anonymity of the signer, the identity of the querier is well preserved. Driver Privacy. Having the randomization technique, the drivers generate (B 1i, B 2i, c i, τ i ) to protect the identities and prevent attackers from corrupting the crowdsourcing responses R i. As the group signature [16] is secure, it is impossible for an attacker to learn the identities of the drivers.

5 TABLE I COMPUTATIONAL BURDEN OF VEHICLES Phases PRIN VSPN Initialization 8T SM 3T SM +T p+t AES Registration 0 6T SM + T AES Querying 6T SM +T AES T SM +T AES Crowdsourcing 3T SM 0 Retrieving 7νT SM +νt AES 4νT p * ν is the number of RSUs that relay the navigation query. Traceability. By using the signature (B 1, B 2, c, τ), the TA can check whether the equation ê(b 2, ĝ) = ê(b 1, X 0 ) ê(b 1, V ) holds or not to determine the identity of the driver. V. PERFORMANCE EVALUATION Firstly, to analyze the computational overhead of the vehicles, we count the number of scalar multiplication in G 1 /G 2, AES encryption/decryption, and bilinear pairing required in PRIN. Let T SM, T AES, T p denote the executing time of the above operations for vehicles, respectively. The number of the operations required in each phase of PRIN are shown in Table I, as well as the operations in VSPN [13]. In PRIN, there is no bilinear pairing computation for vehicles. Although PRIN is less efficient than VSPN in the navigation querying phase, it costs much less time to retrieve the navigation results, as shown in Fig 4 (a). In addition, VSPN does not consider traffic information collection in generating the navigation results. We simulate PRIN on HUAWEI MT2-L01 smartphone with Kirin 910 CPU and 1250M memory. The operation system is Android and the toolset is Android NDK r8d with MIRACL library. The parameter p is approximately 160 bits and the elliptic curve is defined as y = x over F q, where q is 512 bits. The scalar multiplication and AES encryption/decryption take ms and ms, respectively. The executing time of bilinear pairing is ms. Thus, the rough running time of vehicles in system initialization is ms. A vehicle needs to run approximately ms to generate a navigation query and ms to retrieve the navigation result from an RSU. Besides, a vehicle who participates in the crowdsourcing task needs ms to generate a crowdsourcing response. In terms of the communication overhead, our PRIN does not require the RSUs to return the navigation results to the first RSU. Instead, the querying vehicle can retrieve the navigation result from every RSU, and use it to find a proper route to the coverage area of the next RSU. In this way, the communication overhead among RSUs is significantly reduced. Fig 4 (b) shows the comparison results of PRIN and VSPN with respect to the average communication burden between two RSUs. VI. CONCLUSIONS In this paper, we have proposed a privacy-preserving realtime navigation system from vehicular crowdsourcing. Specifically, the RSUs utilize the up-to-date traffic information provided by the vehicles to cooperatively navigate the querying vehicles to the destinations in a distributed manner, without Time Cost (MS) PRIN VSPN Numbers of RSUs Number of RSUs (a) Computational Overhead Fig. 4. Communication Overhead (Bits) PRIN VSPN (b) Communication Overhead Performance Comparison of PRIN and VSPN sacrificing the privacy of the drivers. Besides, the TA can trace the identity of the driver who reports false traffic information. We have demonstrated that our system achieves conditional privacy preservation and is efficient in terms of computational and communication overhead. For the future work, we will develop a privacy-preserving on-street parking sharing system based on vehicular crowdsourcing. REFERENCES [1] K. Moskvitch, Can a city every be traffic jam-free? BBC News, [2] J. Worland, Air pollution kills more than 5 million people around the world every year, Time, [3] D.J. Wu, J. Zimmerman, J. Planul, and J.C. Mitchell, Privacypreserving shortest path computation, in Proc. of NDSS, San Diego, California, USA, [4] Y. Poor, P. Muhletheler, and A. Laouiti, Vehicle ad hoc networks: Applications and related technical issues, IEEE Commun. Surv. 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6 Revision 1: Move the relation works from section VI to the introduction. Revision 2: To make our motivation stronger, we add the cloud into the system model and revise the presentation at the corresponding position of the scheme. The traffic information crowdsourced from vehicles can not only be used for distributively navigation, but also store on the cloud and support centralized navigation, such as google map. Revision 3: We correct some typos and polish some awkward sentences. Revision 4: We add some related references in the paper, such as [5][8][11].