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Register a new userEfficient, Scalable, and Resilient Vehicle-Centric Certificate Revocation List Distribution in VANETs
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Added by
Mohammad Khodaei
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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In spite of progress in securing Vehicular Communication (VC) systems, there is no consensus on how to distribute Certificate Revocation Lists (CRLs). The main challenges lie exactly in (i) crafting an efficient and timely distribution of CRLs for numerous anonymous credentials, pseudonyms, (ii) maintaining strong privacy for vehicles prior to revocation events, even with honest-but-curious system entities, (iii) and catering to computation and communication constraints of on-board units with intermittent connectivity to the infrastructure. Relying on peers to distribute the CRLs is a double-edged sword: abusive peers could pollute the process, thus degrading the timely CRLs distribution. In this paper, we propose a vehicle-centric solution that addresses all these challenges and thus closes a gap in the literature. Our scheme radically reduces CRL distribution overhead: each vehicle receives CRLs corresponding only to its region of operation and its actual trip duration. Moreover, a fingerprint of CRL pieces is attached to a subset of (verifiable) pseudonyms for fast CRL piece validation (while mitigating resource depletion attacks abusing the CRL distribution). Our experimental evaluation shows that our scheme is efficient, scalable, dependable, and practical: with no more than 25 KB/s of traffic load, the latest CRL can be delivered to 95% of the vehicles in a region (50x50 KM) within 15s, i.e., more than 40 times faster than the state-of-the-art. Overall, our scheme is a comprehensive solution that complements standards and can catalyze the deployment of secure and privacy-protecting VC systems.
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In spite of progress in securing Vehicular Communication (VC) systems, there is no consensus on how to distribute Certificate Revocation Lists (CRLs). The main challenges lie exactly in (i) crafting an efficient and timely distribution of CRLs for numerous anonymous credentials, pseudonyms, (ii) maintaining strong privacy for vehicles prior to revocation events, even with honest-but-curious system entities, (iii) and catering to computation and communication constraints of on-board units with intermittent connectivity to the infrastructure. Relying on peers to distribute the CRLs is a double-edged sword: abusive peers could pollute the process, thus degrading the timely CRLs distribution. In this paper, we propose a vehicle-centric solution that addresses all these challenges and thus closes a gap in the literature. Our scheme radically reduces CRL distribution overhead: each vehicle receives CRLs corresponding only to its region of operation and its actual trip duration. Moreover, a fingerprint of CRL pieces is attached to a subset of (verifiable) pseudonyms for fast CRL piece validation (while mitigating resource depletion attacks abusing the CRL distribution). Our experimental evaluation shows that our scheme is efficient, scalable, dependable, and practical: with no more than 25 KB/s of traffic load, the latest CRL can be delivered to 95% of the vehicles in a region (15 x 15 KM) within 15s, i.e., more than 40 times faster than the state-of-the-art. Overall, our scheme is a comprehensive solution that complements standards and can catalyze the deployment of secure and privacy-protecting VC systems.
The security of TLS depends on trust in certificate authorities, and that trust stems from their ability to protect and control the use of a private signing key. The signing key is the key asset of a certificate authority (CA), and its value is based on trust in the corresponding public key which is primarily distributed by browser vendors. Compromise of a CA private key represents a single point-of-failure that could have disastrous consequences, so CAs go to great lengths to attempt to protect and control the use of their private keys. Nevertheless, keys are sometimes compromised and may be misused accidentally or intentionally by insiders. We propose splitting a CAs private key among multiple parties, and producing signatures using a generic secure multi-party computation protocol that never exposes the actual signing key. This could be used by a single CA to reduce the risk that its signing key would be compromised or misused. It could also enable new models for certificate generation, where multiple CAs would need to agree and cooperate before a new certificate can be generated, or even where certificate generation would require cooperation between a CA and the certificate recipient (subject). Although more efficient solutions are possible with custom protocols, we demonstrate the feasibility of implementing a decentralized CA using a generic two-party secure computation protocol with an evaluation of a prototype implementation that uses secure two-party computation to generate certificates signed using ECDSA on curve secp192k1.
SAFE is a data-centric platform for building multi-domain networked systems, i.e., systems whose participants are controlled by different principals. Participants make trust decisions by issuing local queries over logic content exchanged in certificates. The contribution of SAFE is to address a key barrier to practical use of logical trust: the problem of identifying, gathering, and assembling the certificates that are relevant to each trust decision. SAFE uses a simple linking abstraction to organize and share certificates according to scripted primitives that implement the applications trust kernel and isolate it from logic concerns. We show that trust scripting with logical data exchange yields compact trust cores for example applications: federated naming, nested groups and roles, secure IP prefix delegation and routing, attestation-based access control, and a federated infrastructure-as-a-service system. Linking allows granular control over dynamic logic content based on dependency relationships, enabling a logic server to make secure inferences at high throughput.
In this paper, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicles anonymous location. Proofs sent from multiple consecutive RSUs is used to create vehicle trajectory which is used as vehicle anonymous identity. Also, one RSU is not able to issue trajectories for vehicles, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. So, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. Then, during any reported event, e.g., road congestion, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle; therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate to Sybil attacks with low false negative and acceptable communication and computation overhead.
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves data island problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
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