Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user's context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.

It has been a consensus that a certain relationship exists between personal emotions and usage pattern of the smartphone. Based on users’ emotions and personalities, more and more applications are developed to provide intelligent automation services on the smartphone, such as music recommendations or stranger introductions on social networking sites. Most existing work studies this relationship by learning large amounts of samples, which are manually labeled and collected from smartphone users. The manual labeling process, however, is very time-consuming and labor-intensive. To address this issue, we propose iSelf, a system that provides a general service of automatic detection of a user’s emotions in cold-start conditions with a smartphone. With the technology of transfer learning, iSelf achieves high accuracy given only a few labeled samples. We also embed a hybrid public/personal inference engine and validation system into iSelf, to make it maintain updates continuously. Through extensive experiments in real traces, the inferring accuracy is tested above 74% and can be improved increasingly through validation and updates. The application program interface has been open online for other developers.

Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or under-select modulation schemes which limits throughput performance. To fill-in the gap, we propose time-line modulation (TiM), a novel three-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200 percent.

Crowdsensing applications require individuals toshare local and personal sensing data with others to produce valuableknowledge and services. Meanwhile, it has raised concernsespecially for location privacy. Users may wish to prevent privacyleak and publish as many non-sensitive contexts as possible.Simply suppressing sensitive contexts is vulnerable to the adversariesexploiting spatio-temporal correlations in users' behavior.In this work, we present PLP, a crowdsensing scheme whichpreserves privacy while maximizes the amount of data collectionby filtering a user's context stream. PLP leverages a conditionalrandom field to model the spatio-temporal correlations amongthe contexts, and proposes a speed-up algorithm to learn theweaknesses in the correlations. Even if the adversaries are strongenough to know the filtering system and the weaknesses, PLPcan still provably preserves privacy, with little computationalcost for online operations. PLP is evaluated and validated overtwo real-world smartphone context traces of 34 users. Theexperimental results show that PLP efficiently protects privacywithout sacrificing much utility.

To meet the demand of more intelligent automation services on smartphone, more and more applications are developed based on users' emotion and personality. It has been a consensus that a relationship exists between personal emotions and usage pattern of smartphone. Most of existing work studies this relationship by learning manually labeled samples collected from smartphone users. The manual labeling process, however, is time-consuming, labor-intensive and money-consuming. To address this issue, we propose iSelf, a system which provides a general service of automatic detection for user's emotions in cold-start conditions with smartphone. Using transfer learning technology, iSelf achieves high accuracy given only a few labeled samples. We also develop a hybrid public/personal inference engine and validation system, so as to make iSelf maintain continuous update. Through extensive experiments, the inferring accuracy is tested about 75% and can be improved increasingly through validation and update.

Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, if not carefully designed, taxi-sharing may cause more harm than benefit - it becomes harder to strike the balance between driver's profit and passenger's quality of service (e.g. travel time, number of strangers that share a taxi, etc.). In this paper, we propose a QoS-aware taxi-sharing system design - QA-Share - by addressing two important challenges. First, QA-Share aims to maximize driver profit and user experience at the same time. Second, QA-Share continuously optimizes these two metrics by dynamically adapting its schedule as new requests arrive, without entering an oscillation state. To address these two challenges, we have formulated the optimization problem using integer linear programming, and derived the optimal solution under a small system scale. When the number of requests and taxis becomes large, we have devised a heuristic algorithm that has a much faster execution time. We have also studied how to minimize oscillations caused by schedule re-calculations by dynamically tuning the update threshold. We have evaluated our approach with real-world dataset in a Chinese city - ZhenJiang - which contains the GPS traces recorded by over 3,000 taxis during a period of three months in 2013. Our results show that the QoS and profit is increased by 38% compared to earlier schemes.

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.

Channel condition varies frequently in wireless networks. To achieve good performance, devices need rate adaptation. In rate adaptation, choosing proper modulation schemes based on channel conditions is vital to the transmission performance. However, due to the natural character of discrete modulation types and continuous varied link conditions, we cannot make a one-to-one mapping from modulation schemes to channel conditions. This matching gap causes either over-select or under-select modulation schemes which limits throughput performance. To fill-in the gap, we propose TiM (Time-line Modulation), a novel 3-Dimensional modulation scheme by adding time dimension into current amplitude-phase domain schemes. With estimation of channel condition, TiM changes base-band data transmission time by artificially interpolating values between original data points without changing amplitude-phase domain modulation type. We implemented TiM on USRP2 and conducted comprehensive simulations. Results show that, compared with rate adaptation choosing from traditional modulation schemes, TiM can improve channel utilization up to 200%.