We propose the first auctioneer-free sealed-bid auction protocol with a linear computation and communication complexity O(c), c being the bit length of the bid price. Our protocol, called Self-Enforcing Auction Lot (SEAL), operates in a decentralized setting, where bidders jointly compute the maximum bid while preserving the privacy of losing bids. In our protocol, we do not require any secret channels between participants. All operations are publicly verifiable; everyone including third-party observers is able to verify the integrity of the auction outcome. Upon learning the highest bid, the winner comes forward with a proof to prove that she is the real winner. Based on the proof, everyone is able to check if there is only one winner or there is a tie. While our main protocol works with the first-price sealed-bid, it can be easily extended to support the second-price sealed-bid (also known as the Vickrey auction), revealing only the winner and the second highest bid, while keeping the highest bid and all other bids secret. To the best of our knowledge, this work establishes to date the best computation and communication complexity for sealed-bid auction schemes without involving any auctioneer.

In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users’ perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.

Routing attacks are a major security issue for Internet of Things (IoT) networks utilising routing protocols, as malicious actors can overwhelm resource-constrained devices with denial-of-service (DoS) attacks, notably rank and blackhole attacks. In this work, we study the impact of the combination of rank and blackhole attacks in the IPv6 routing protocol for low-power and lossy (RPL) networks, and we propose a new security framework for RPL-based IoT networks (SRF-IoT). The framework includes a trust-based mechanism that detects and isolates malicious attackers with the help of an external intrusion detection system (IDS). Both SRF-IoT and IDS are implemented in the Contiki-NG operating system. Evaluation of the proposed framework is based on simulations using the Whitefield framework that combines both the Contiki-NG and the NS-3 simulator. Analysis of the simulations of the scenarios under active attacks showed the effectiveness of deploying SRF-IoT with 92.8% packet delivery ratio (PDR), a five-fold reduction in the number of packets dropped, and a three-fold decrease in the number of parent switches in comparison with the scenario without SRF-IoT. Moreover, the packet overhead introduced by SRF-IoT in attack scenarios is minimal at less than 2%. Obtained results suggest that the SRF-IoT framework is an efficient and promising solution that combines trust-based and IDS-based approaches to protect IoT networks against routing attacks. In addition, our solution works by deploying a watchdog mechanism on detector nodes only, leaving unaffected the operation of existing smart devices.