Abstract Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences–some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate associated privacy risks result in an imbalanced trade-off between privacy and utility. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to images of the user’s face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.

Longer range, in rural/urban IoT networks, allow a large geographical coverage with only a few base-stations, making their deployment and operation economical. In this paper we explore the 150-174 MHz spectrum for long range IoT networks comprising unlicensed MURS and licensed VHF narrowbands. Range in these bands is boosted by the lower RF frequencies as well as higher transmit powers allowed by the FCC. Through a 400 sq km wide area deployment study, we show that, these spectrum bands can provide > 20× the geographical coverage than that in the 900 MHz ISM band LoRa. Increased range translates to greater uplink IoT device traffic. The key contribution of this paper is a novel technique - Blind Distributed MU-MIMO, that allows capacity to scale with the number of antennas (base- stations) while not requiring any coordinated channel measurements between the devices and IoT base- stations. This requirement is crucial since in IoT networks power constrained IoT devices typically sleep and wake up to transmit short messages in response to unpredictable events without any coordination with the base-stations. We demonstrate the efficacy of Blind Distributed MU-MIMO through a real wide area deployment.

Abstract Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user’s environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that protects the user’s privacy against a 16-microphone adversary with only four commodity obfuscators, regardless of the adversary’s position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.

Mobile virtual reality (VR) headsets, such as Google Cardboard and Samsung GearVR, can reuse a smartphone as near-eye display to create immersive experience. But such devices barely support any user interaction, even for simple tasks such as menu selection and single-character input. In this paper, we design Inkput, a simple passive interface attached to the unexploited backside of the headset to enable touch sensing. Inkput is a piece of paper substrate with carbon ink patterns printed atop. It leverages the column of electrodes near the edge of the smartphone touchscreen to sense multi-touch on the 2D space, and is even able to locate finger hovering. Our experiments demonstrate that Inkput can precisely detect touch positions with mm-level precision. Our case studies in actual VR applications also verify that Inkput can support common VR interactions and can even outperform high-end handheld controllers in terms of efficiency.

Watching videos online has become prevalent in our daily activities. While there are extensive measurements studying about video on demand, IPTV, live streaming, etc., most of them focus their analysis on a single content provider (CP). Various user behaviors over multiple different CPs are yet to be explored. In this paper, we collect about ten million viewing records of the six most popular video CPs through a major Internet service provider (ISP) in Shanghai, China, and examine the user behaviors and access patterns with an emphasis on comparing them among different CPs in the same environments. Our analysis reveals the day-scale temporal patterns, the diversified device-wise preferences, and the city-scale geographical features of the different CPs, and identifies the high relevance between the user behaviors and the contents of the videos. Moreover, we find that user migration across multiple CPs is prevalent and highly influenced by different features of CPs. We believe that our observations and findings can provide valuable insights for CPs to enhance their user experience as well as ISPs to optimize the network architecture.

Device-to-device (D2D) communication, which enables direct transmissions between mobile devices to improve spectrum efficiency, is one of the preferable candidate technologies for the next generation cellular network. Network coding, on the other hand, is widely used to improve throughput in ad hoc networks. Thus, the performance of D2D communications in cellular networks can potentially benefit from network coding. Aiming to improve the achievable capacity of D2D communications, we propose a system with inter-session network coding enabled to assist D2D transmissions. We formulate the joint problem of relay selection and resource allocation in network coding assisted D2D communications, and obtain the overall capacity of the network under complex interference conditions as a function of the relay selection and resource allocation. To solve the formulated problem, we propose a two-level decentralized approach termed NC-D2D, which solves the relay selection and resource allocation problems alternatively to obtain stable solutions for these two problems. Specifically, a coalition formation game associates relays with D2D pairs to enable network coding aided transmissions, and a greedy algorithm based game allocates limited cellular resources to D2D pairs and relays in NC-D2D, respectively. The performances of the proposed scheme is evaluated through extensive simulations to prove its superiority.

With the explosive growth of mobile data demand, there has been an increasing interest in deploying small cells of higher frequency bands underlying the conventional homogeneous macrocell network, which is usually referred to as heterogeneous cellular networks, to significantly boost the overall network capacity. With vast amounts of spectrum available in the millimeter-wave (mmWave) band, small cells at mmWave frequencies are able to provide multigigabit access data rates, whereas the wireless backhaul in the mmWave band is emerging as a cost-effective solution to provide high backhaul capacity to connect access points of the small cells. In order to operate the mobile network optimally, it is necessary to jointly design the radio access and backhaul networks. Meanwhile, direct transmissions between devices should be also considered to improve system performance and enhance user experience. In this paper, we propose a joint transmission scheduling scheme for the radio access and backhaul of small cells in the mmWave band, termed D2DMAC, where a path selection criterion is designed to enable device-to-device transmissions for performance improvement. In D2DMAC, a concurrent transmission scheduling algorithm is proposed to fully exploit spatial reuse in mmWave networks. Through extensive simulations under various traffic patterns and user deployments, we demonstrate that D2DMAC achieves near-optimal performance in some cases and outperforms other protocols significantly in terms of delay and throughput. Furthermore, we also analyze the impact of path selection on the performance improvement of D2DMAC under different selected parameters.