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Dieses Kapitel geht den Herausforderungen und Lösungen zur Sicherung intelligenter Transportsysteme (ITS) durch die Integration von Blockchain-Technologie und homomorpher Verschlüsselung nach. Er untersucht die Rolle des Nebel-Computing bei der Ermöglichung eines Echtzeit-Verkehrsmanagements mit geringer Latenzzeit und zeigt die Grenzen des traditionellen Cloud Computing auf. Das vorgeschlagene System umfasst einen mehrstufigen Prozess, der von einer vertrauenswürdigen Behörde orchestriert wird und sich auf die Registrierung von Fahrzeugen und Nebelknoten, Authentifizierung und sichere Kommunikation konzentriert. Der Text diskutiert auch den Einsatz intelligenter Verträge zur Verwaltung von Fahrzeugdaten und zur Gewährleistung der Privatsphäre. Es werden Leistungsanalysen und Machbarkeitsstudien vorgelegt, die die Effizienz und Sicherheit des vorgeschlagenen Ansatzes belegen. Das Kapitel schließt mit einer Diskussion über die praktische Umsetzung des Programms, einschließlich der Gaskosten und Einsatzstrategien für die Ethereum-Blockchain.
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Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
Growing traffic volumes in cities demand new ways to manage routes. Current Intelligent Transport Systems (ITS) face safety and privacy issues, high data costs, and security vulnerabilities. We propose a lightweight system using fog computing and homomorphic encryption. Vehicles anonymously share encrypted travel data with a trusted fog node, allowing the Traffic Control Centre to safely manage congestion without compromising individual privacy. Experimental results show our system outperforms existing approaches, offering a secure and efficient solution for future ITS.
1 Introduction
Due to surging vehicles and communication advancements, Intelligent Transport Systems (ITS) research is flourishing. ITS involves vehicles, roadside units (RSUs), and trusted authorities (TAUs) collaborating for a smart traffic network with V2V and V2I communication. V2V communication enhances safety, while RSU infrastructure facilitates V2I for traffic services [1]. However, security and privacy are major concerns in ITS due to public communication channels, making message authentication crucial for both V2V and V2I [2, 3]. Existing privacy-preserving schemes have limitations. The rise in vehicles demands efficient data processing. Cloud computing suffers from high latency due to distant servers [4, 5]. Fog computing with Fog Nodes (FNs) at the network edge tackles this by offering low latency, wider reach, and real-time processing for efficient traffic management [6‐8] (Fig. 1).
While PKI-based schemes for ACPP have been explored, they face limitations in storage, revocation costs, and validation delays. Identity-based schemes have sought to address these issues through efficient batch signature verification, secret sharing, and substituted vehicles. However, privacy-preserving methods often rely on computationally expensive pairing-based encryption. Our work investigates Group Diffie-Hellman encryption as a more efficient alternative.
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2.2 Blockchain-Based Verification Strategies
While prior research has investigated blockchain for VANET verification, limitations persisted, including reliance on certifying agencies, frequent CA updates, limited rogue vehicle tracking, and domain-specific designs. Other efforts focused on fog-based validation, smart contract-based key management, and zero-knowledge proofs for privacy.
2.3 Systems of Verification that Utilize Fog
Many Recent works leverage fog computing in VANET’s, proposing a real-time roadway monitoring architecture and designing a secure aggregation scheme for fog-based safety alerts, ensuring both location awareness and privacy preservation. Some introduce a privacy-preserving contracting framework for vehicles using fog nodes. While some developing a compact verification technique for location-aware privacy protection under specific conditions in fog-based VANETs. These efforts showcase the potential of fog computing to enhance VANET security and efficiency.
3 Description and the Backstory
Homomorphic Encryption (HE) safeguards sensitive data by allowing computations directly on encrypted data. Public and private keys, generated using large primes, facilitate encryption and decryption [1]. Blockchain technology, known for its use in cryptocurrency, offers a secure and tamper-proof method for storing data through linked blocks. Its cryptographic techniques ensure data immutability [2]. The Diffie-Hellman (DH) algorithm [6] enables secure key exchange on insecure networks. It leverages a mathematical group and a large prime number (p) for two parties to establish a shared secret key (K) without prior knowledge. The security of blockchain technology relies on the difficulty of solving the discrete logarithm problem (DLP) [9]. Smart contracts, envisioned by Nick Szabo, propose a revolutionary key management solution for vehicles using blockchains like Ethereum. These tamper-proof systems manage tasks like owner verification, unlocking, and access control (Figs. 2, 3 and 4), fundamentally changing how we trust and share car keys. This section lays the groundwork for understanding the technical aspects of the proposed secure Intelligent Transport System (ITS).
This section outlines a multi-step process orchestrated by TAU. Firstly, TAU bootstraps the system and handles registration requests from both vehicles and fog nodes. Next, vehicles authenticate and register themselves, while fog nodes securely combine their submitted routes into a single encrypted bundle. The Traffic Management Center (TMC) then receives this combined ciphertext, decrypts it, and utilizes the decoded routes to manage traffic flow. A straightforward mechanism is also implemented to allow for password changes and the removal of unauthorized vehicles. For your reference, key notations and their corresponding meanings are provided in Table 1.
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4.1 Automobile Registration
Vehicles undergo a secure registration process with TAU before leaving the manufacturer. This involves encrypted communication to establish a pseudo-identity and obtain a confidential key. Details of this interaction are explained below.
1.
The driver chooses a password (PWV) and user ID (UID), calculating \(UPWV = h1(UID \parallel PWV)\). Encrypted credentials (IDV, UPWV) are sent to TAU for secure registration.
2.
TAU verifies if the vehicle (V) is registered or blacklisted. If not, registration proceeds.
3.
TAU generates a secret key (skv) for V, a pseudonym (PIDV), and additional cryptographic elements.
4.
TAU transmits these elements (Bi, Ci, skV, PIDV, n, g) to V for secure storage and uploads PIDV and the public key (VPK) to a smart contract.
The fog node securely communicates offline with TAU to obtain a private key for the subsequent signing process. Details of this interaction are explained below.
1.
Fog node (FN) selects an identifier (IDF) and sends a registration request to TAU.
2.
Upon receiving the request, TAU checks if the IDF is available. If unavailable, TAU rejects the request. Otherwise, TAU generates a secret key (\(skf \in \mathbb {Z}_q^*\)) and computes the corresponding public key (\(fpk = skf P\)).
3.
The TAU then sends skf, fpk and broadcasts fpk onto the fog node.
4.
FN completes registration by storing the secret key (skf) securely.
Table 1.
List of Notations and Definitions
Notations
Definitions
TAU
Trust authority
TMC
Traffic control center
V, FN
i-th vehicle and j-th fog node, respectively
IDV, IDF
Real identity of vehicle and fog node, respectively
UID
Real identity of user
PWV
Password of vehicle
s, Ppub
Private key and public key of TAU, respectively
skv, skf
Private key of vehicle and fog node, respectively
VPK, fpk
Public key of vehicle and fog node, respectively
PIDV
Pseudonym of vehicle
Ti1, Tj1, Tj2
Latest timestamp
M
Message sent by vehicle and fog node
concat
Concatenation operation
xor
Exclusive-OR operation
DH
Diffie Hellman algorithm
G
Additive group with order q
P
Generator of the group G
h
Secure one-way hash functions
4.3 Sign-In Stage
This phase is to check the legality of the automobile’s user.
1.
User in vehicle \(U_i\) enters \(\{ID_V, PW_V, UID\}\) into the On-Board Unit (OBU) of vehicle V.
2.
The V calculates \(U_{PW_V} = h_1(UID||PW_V)\) and \(A_i = B_i\oplus h_4(ID_V)\). It then verifies if \(C_i = h_3(A_i || U_{PW_i})\) holds. If the equation is true, the login is approved, and V proceeds. Otherwise, V rejects the attempt immediately.
4.4 Request for Route from the Fog Node
1.
The \(\textrm{FN}\) generates a random value xz, calculates \(Xz = xz \cdot P\). It then creates a signature \(F1 = \textrm{skf} + xz h2(\mathrm {ID_F} || \textrm{RS} || T{j1}) \mod q\) using its private key and a formula involving its identity,route request list, and timestamp. It then broadcasts \(M1 = \{\textrm{IDF}, Xz, \sigma _{F1}, \textrm{RS}, T{j1}\}\) to nearby vehicles.
2.
Upon receiving transmission M1 from \(\textrm{FN}\), V validates timestamp Tj1. If valid, V verifies Eq(1). If confirmed, V acknowledges communication and proceeds.
4.5 Reporting Route to the Fog Node
For privacy, vehicles encrypt routes before sharing them with the fog node. This homomorphic encryption allows manipulation and aggregation of the encrypted data for efficient package handling, without decryption. This protects user privacy by hiding individual travel patterns from the fog node.
1.
V efficiently reports its route information in binary format: \(\{F_1(V), F_2(V), ..., F_m(V)\}\), each \(F_i(V)\) is a bitwise where the final bit indicates V’s intention to use route NO.i. The bit length of \(F_i(V)\) aligns with the \(\operatorname {base}2\) log of the maximum V’s supported by a fog node. (e.g. \(\{000, 001, 000, 001\}\)) signifies travel on Routes No. 2 and 4.
2.
V selects random \(r_i \in \mathbb {Z}^q\) and encrypts it as \(\varphi _i = g \cdot F_0(i) \mathbin | F_1(i) \mathbin | F_m(i) \cdot r_n i \bmod n^2\). V chooses another random \(d_i \in \mathbb {Z}^q\) and calculates \(D_i = d_i \cdot P, where i = h_5(PID_V \mathbin | D_i \mathbin | \varphi _i \mathbin | T{i1})\). V creates a signature \(V = sk_v + d_i \cdot \beta _i \bmod q\), wherein \(T_{i1}\) .
3.
At the very least, V sends the information \(M_v = \{{\sigma _V, i, D_i, PID_V, T_{i1}}\}\) to FN.
4.6 Management of the Traffic Route
The TMC first validates the timestamp Tj1 before confirming the validity of Eq. (1). If Tj1 is not valid, the TMC denies the communication. Otherwise, it proceeds with the subsequent actions.
Following this, the TMC utilizes the private \((\lambda , \mu )\) to decode all aggregated travel routes \(\phi _{sum}\) by determining \(L(\phi _{\lambda sum \mod n_2}) \cdot \mu \mod n\), where \(\mu = (L(g^{\lambda \mod n_2}))^{-1} \mod n\), \(L(x) = \sum _{i=1}^{n}(x - i)/n\), and recovers the route
information.
\(RI = F_0(0) \cdot \sum _{i=1}^{k}P(F_0(i)^{\lambda \mod n_2}) \cdot \sum _{i=1}^{m}\sum _{j=1}^{k}P(F_j(i)^{\lambda \mod n_2})\). Though meticulously dissecting our final findings to grasp the plan’s entirety, the precise forces propelling it remain elusive. For every automobile’s journey. every route has 5 portions, each of which is denoted by 3 bits. This is based upon the assumption that 6 automobiles would give their own future driving routes. V3’s path is 001 000 001 000 001, and the TMC may obtain the total number \(\sum _{i=0}^5 F_1(V)=2,\cdots ,\sum _{i=0}^5 F_5(V) = 6\).
4.7 Vehicle Revoke
The smart contract governing vehicle V’s voting rights automatically revokes them (via DeletePK) if, a) V encapsulates and broadcasts a voting transaction, exceeding a predefined threshold (VCT>THD), or b) TAU finds V’s public key invalid during routine checks. This ensures bad actors are swiftly penalized within the trusted execution environment.
5 Performance Analysis
We evaluated our GDH-based key generation algorithm for high-mobility VANET’s using ns3 and SUMO on a simulated 5 km interstate network with 40 cars. Focusing on group key generation (KG) time, we found it increases with vehicle count due to more computations but is minimally affected by speed. G and G1 components are 320 and 1024 bits, respectively, while our 3-stage authentication incurs 3040 bits of overhead. Compared to Shen (6592 bits), Khaled (4736 bits), and Cui (2720 bits), our scheme offers a competitive overhead while providing strong security for high-mobility VANETs. This is the first simulated GDH-based KG with MIM defense for such networks, establishing a benchmark for future research.
5.1 Blockchian’s Feasibility Analysis in Practise
We validated our car data management smart contract on the Rinkeby testnet, deploying it via Solidity and assessing gas costs for key tasks like public key management. Using Remix and querying methods, we found deployment a one-time cost of about USD 41.50, and common operations under USD 5.71. These reasonable costs demonstrate the feasibility of our approach for secure and cost-effective car data management (Table 2).
Table 2.
Gas Cost of Smart Contract
Operation
Gas Used
Actual Cost (ether)
USD
deploy
431800
0.022198040
41.4998
update
59375
0.003051900
5.7141
query
25742
0.001324936
2.4812
delete
37580
0.001934640
3.6235
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