Sparse bundle adjustment (SBA) is a key but time- and memory-consuming step in three-dimensional (3D) reconstruction. In this paper, we propose a 3D point-based distributed SBA algorithm (DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment (A-DSBA) to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism (SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm (running on eight nodes with 48 cores) is up to 41 times that of the serial SBA (running on a single node).

Approximate$k$Nearest Neighbours (A$k$NN) search is widely used in domains such as computer vision and machine learning. However, A$k$NN search in high-dimensional datasets does not scale well on multicore platforms, due to its large memory footprint. Parallel A$k$NN search using space subdivision for filtering helps reduce the memory footprint, but its loss of precision is unstable. In this paper, we propose a new data filtering method—PCAF—for parallel A$k$NN search based on principal component analysis. PCAF improves on previous methods, demonstrating sustained, high scalability for a wide range of high-dimensional datasets on both Intel and AMD multicore platforms. Moreover, PCAF maintains highly precise A$k$NN search results.

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the current trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Alexa, Siri, and Google Now, have become our everyday fixtures, especially when/where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. The open nature of the voice channel makes voice assistants difficult to secure, and hence exposed to various threats as demonstrated by security researchers. To defend against these threats, we present VAuth, the first system that provides continuous authentication for voice assistants. VAuth is designed to fit in widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees the voice assistant to execute only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve 97% detection accuracy and less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts various practical attacks, such as replay attacks, mangled voice attacks, or impersonation attacks. It also incurs low energy and latency overheads and is compatible with most voice assistants.

Approximate k Nearest Neighbours (AkNN) search is widely used in domains such as computer vision and machine learning. However, AkNN search in high dimensional datasets does not work well on multicore platforms. It scales poorly due to its large memory footprint. Current parallel AkNN search using space subdivision for filtering helps reduce the memory footprint, but leads to loss of precision. We propose a new data filtering method -- PCAF -- for parallel AkNN search based on principal components analysis. PCAF improves on previous methods by demonstrating sustained, high scalability for a wide range of high dimensional datasets on both Intel and AMD multicore platforms. Moreover, PCAF maintains high precision in terms of the AkNN search results.

Smartphone apps are becoming a popular vehicle to collect users' personal interests, demographics and other private information. Due to lack of regulation, a curious party can covertly link and aggregate sensitive information from independent sources (sessions or apps) over time to conduct unwanted user profiling, targeted advertising or surveillance. Such unregulated aggregation is rooted at the non-existence of unlinkability in the mobile ecosystem. On one hand, the mobile ecosystem is over-populated with various persistent identifiers and fueled by the abundance of user information; on the other hand, users only expect app usages that are functionally-dependent to be linkable. To bridge this gap, we propose a practical solution, called Mask, that allows users to negotiate to what extent his behavior can be linked and aggregated. Specifically, Mask introduces a set of private execution modes which enable different levels of unlinkability. Mask is a user-level solution and does not require any change in the existing ecosystem, thus allowing for easy deployment. We present the technical details and challenges of our user-level implementation and evaluate its runtime performance as well as applicability.

The approach for fast application relaunching on the current Android system is to cache background applications in memory. This mechanism is limited by the available memory size. In addition, the application state may not be easily recovered. We propose a prototype system, MARS, to enable page swapping and cache more applications. MARS can speed up the application relaunching and restore the application state. As a new page swapping design for optimizing application relaunching, MARS isolates Android runtime Garbage Collection (GC) from page swapping for compatibility and employs several flash-aware techniques for swap-in speedup. Two main components of MARS are page slot allocation and read/write control. Page slot allocation reorganizes page slots in swap area to produce sequential reads and improve the performance of swap-in. Read/Write control addresses the read/write interference issue by reducing concurrent and extra internal writes. Compared to the conventional Linux page swapping, these two components can scale up the read bandwidth up to about 3.8 times. Application tests on a Google Nexus 4 phone show that MARS reduces the launching time of applications by 50 - 80 percent. The modified page swapping mechanism can outperform the conventional Linux page swapping up to four times.

Over the past few years, Proximity-based People Discovery (PBPD) services, typically known as Nearby Friends,have been increasingly popular among geosocial apps. Unlike many unsuccessful predecessors which directly pinpoint users' exact locations on the map, PBPD services provide coarse-grained (discretized) proximity information, such as "Jennifer is within 2 miles," striking a useful balance between privacy and functionality. Considering PBPD's business potential, many companies including Facebook have been trying to promote this feature and instill the perception in mobile users that coarse-grained proximity information is innocuous to share. Here, we propose a novel positioning attack which can locate end-users of PBPD services with high precision using only coarse-grained (discretized) proximity information. This attack requires neither specialized hardware nor server-side collusion and can be easily automated. Based on this attack, we design and implement Geosocial Positioning System (GsPS) and show that GsPS can effectively locate users with high precision (10m) in a matter of a few minutes under real-world settings, and is capable of performing effective city-scale scanning and long-term profiling at low costs. The public and the social network industry should therefore be aware of the potential risk introduced by this attack and consider use of PBPD services with caution.