Biblio

Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.

To prevent unauthorized parties from accessing data stored on their smartphones, users have the option of enabling a "lock screen" that requires a secret code (e.g., PIN, drawing a pattern, or biometric) to gain access to their devices. We present a detailed analysis of the smartphone locking mechanisms currently available to billions of smartphone users worldwide. Through a month-long field study, we logged events from a panel of users with instrumented smartphones (N=134). We are able to show how existing lock screen mechanisms provide users with distinct tradeoffs between usability (unlocking speed vs. unlocking frequency) and security. We find that PIN users take longer to enter their codes, but commit fewer errors than pattern users, who unlock more frequently and are very prone to errors. Overall, PIN and pattern users spent the same amount of time unlocking their devices on average. Additionally, unlock performance seemed unaffected for users enabling the stealth mode for patterns. Based on our results, we identify areas where device locking mechanisms can be improved to result in fewer human errors – increasing usability – while also maintaining security.

This study examines the effectiveness of virtual reality technology at creating an immersive user experience in which participants experience first hand the extreme negative consequences of smartphone use while driving. Research suggests that distracted driving caused by smartphones is related to smartphone addiction and causes fatalities. Twenty-two individuals participated in the virtual reality user experience (VRUE) in which they were asked to drive a virtual car using a Oculus Rift headset, LeapMotion hand tracking device, and a force feedback steering wheel and pedals. While driving in the simulation participants were asked to interact with a smartphone and after a period of time trying to manage both tasks a vehicle appears before them and they are involved in a head-on collision. Initial results indicated a strong sense of presence was felt by participants and a change or re-enforcement of the participant's perception of the dangers of smartphone use while driving was observed.

Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87.69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.

Data security has always been a major concern and a huge challenge for governments and individuals throughout the world since early times. Recent advances in technology, such as the introduction of cloud computing, make it even a bigger challenge to keep data secure. In parallel, high throughput mobile devices such as smartphones and tablets are designed to support these new technologies. The high throughput requires power-efficient designs to maintain the battery-life. In this paper, we propose a novel Joint Security and Advanced Low Density Parity Check (LDPC) Coding (JSALC) method. The JSALC is composed of two parts: the Joint Security and Advanced LDPC-based Encryption (JSALE) and the dual-step Secure LDPC code for Channel Coding (SLCC). The JSALE is obtained by interlacing Advanced Encryption System (AES)-like rounds and Quasi-Cyclic (QC)-LDPC rows into a single primitive. Both the JSALE code and the SLCC code share the same base quasi-cyclic parity check matrix (PCM) which retains the power efficiency compared to conventional systems. We show that the overall JSALC Frame-Error-Rate (FER) performance outperforms other cryptcoding methods by over 1.5 dB while maintaining the AES-128 security level. Moreover, the JSALC enables error resilience and has higher diffusion than AES-128.

With the proliferation of WiFi-enabled devices, people expect to be able to use them everywhere, be it at work, while commuting, or when visiting friends. In the latter case, home owners are confronted with the burden of controlling the access to their WiFi router, and usually resort to simply sharing the password. Although convenient, this solution breaches basic security principles, and puts the burden on the friends who have to enter the password in each and every of their devices. The use of social networks, specifying the trust relations between people and devices, provides for a more secure and more friendly authentication mechanism. In this paper, we progress the state-of-the-art by abandoning the centralized solution to embed social networks in WiFi authentication; we introduce EAP-SocTLS, a decentralized approach for authentication and authorization of WiFi access points and other devices, exploiting the embedded trust relations. In particular, we address the (quadratic) search complexity when indirect trust relations, like the smartphone of a friend's kid, are involved. We show that the simple heuristic of limiting the search to friends and devices in physical proximity makes for a scalable solution. Our prototype implementation, which is based on WebID and EAP-TLS, uses WiFi probe requests to determine the pool of neighboring devices and was shown to reduce the search time from 1 minute for the naive policy down to 11 seconds in the case of granting access over an indirect friend.
 

Random numbers represent one of the most sensible part of a cryptographic system, since the cryptographic keys must be entirely based on them. The security of a communication relies on the key that had been established between two users. If an attacker is able to deduce that key, the communication is compromised. This is why key generation must completely rely on random number generators, so that nobody can deduce the. This paper will describe a set of public and free Random Number Generators (RNG) within Android-based Smartphones by exploiting different sensors, along with the way of achieving this scope. Moreover, this paper will present some conclusive tests and results over them.
 

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

Suppose that you are at a music festival checking on an artist, and you would like to quickly know about the song that is being played (e.g., title, lyrics, album, etc.). If you have a smartphone, you could record a sample of the live performance and compare it against a database of existing recordings from the artist. Services such as Shazam or SoundHound will not work here, as this is not the typical framework for audio fingerprinting or query-by-humming systems, as a live performance is neither identical to its studio version (e.g., variations in instrumentation, key, tempo, etc.) nor it is a hummed or sung melody. We propose an audio fingerprinting system that can deal with live version identification by using image processing techniques. Compact fingerprints are derived using a log-frequency spectrogram and an adaptive thresholding method, and template matching is performed using the Hamming similarity and the Hough Transform.