Cruising for a vacant and economical parking spot causes not only time-consuming and frustrating driving experiences, but fuel waste and air pollution. Public parking spots in crowded cities are scarce and expensive. On the contrary, private parking spots usually have low utilization rates, and the spot suppliers are willing to provide their extra parking resources due to a maintenance cost by charging parking fees. Given this situation, it is imperative to call for a smart parking system that collects and provides private parking spots (e.g., around home or workplace) to ease public parking concerns. However, when the suppliers (drivers) are providing (querying for) parking spots, their privacy (e.g., location, identity) is inevitable to be disclosed and existing parking schemes cannot achieve anonymous authentication and anonymous payment simultaneously. To tackle these problems, we propose an anonymous smart-parking and payment (ASAP) scheme in vehicular networks. Specifically, we use short randomizable signature to provide anonymity and conditional privacy. We achieve quick result matching with hashmap and anonymous payment with E-cash. Security analysis and experimental results show that ASAP can protect privacy in a conditional way and has low computational costs and communication overhead.

Public-key infrastructure (PKI) provides the essential foundation for public-key cryptography, and security is often bootstrapped from a PKI. Standardization bodies, organizations, researchers, and experts reached a consensus that deploying Vehicular PKI (VPKI) can ensure the security and privacy requirements of Cooperative Intelligent Transportation Systems (C-ITS). Given the importance of VPKI, researchers and experts have worked to ensure VPKI meets the high security and privacy requirements of C-ITS. This survey focuses on VPKI schemes designed to securely and privately manage and revoke public-key certificates in three parts. The first part presents evaluation metrics that help to compare the existing VPKI proposals systematically. The second part classifies VPKI schemes and provides an overview of each. The third part focuses on observations made using the evaluation metrics through a systematic comparison of VPKI schemes. Finally, suggestions for future VPKI research are presented.

Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional blackboxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models which correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is therefore crucial to understand how and which models conform to human understanding of tasks. In this paper we present a large-scale crowdsourcing study that reveals and quantifies the dissonance between human and machine understanding, through the lens of an image classification task. In particular, we seek to answer the following questions: Which (well performing) complex ML models are closer to humans in their use of features to make accurate predictions? How does task difficulty affect the feature selection capability of machines in comparison to humans? Are humans consistently better at selecting features that make image recognition more accurate? Our findings have important implications on human-machine collaboration, considering that a long term goal in the field of artificial intelligence is to make machines capable of learning and reasoning like humans.

Cyber-attacks are becoming more common against Internet users due to the increasing dependency on online communication in their daily lives. X.509 Public-Key Infrastructure (PKIX) is the most widely adopted and used system to secure online communications and digital identities. However, different attack vectors exist against the PKIX system, which attackers exploit to breach the security of the reliant protocols. Recently, various projects (e.g., Let’s Encrypt and Google Certificate Transparency) have been started to encrypt online communications, fix PKIX vulnerabilities, and guard Internet users against cyber-attacks. This survey focuses on classical PKIX proposals, certificate revocation proposals, and their implementation on blockchain as well as ledger technologies. First, we discuss the PKIX architecture, the history of the World Wide Web, the certificate issuance process, and possible attacks on the certificate issuance process. Second, a taxonomy of PKIX proposals, revocation proposals, and their modern implementation is provided. Then, a set of evaluation metrics is defined for comparison. Finally, the leading proposals are compared using 15 evaluation metrics and 13 cyber-attacks before presenting the lessons learned and suggesting future PKIX and revocation research.