The continuous publication of aggregate statistics over crowd-sourced data to the public has enabled many data mining applications. Existing systems usually rely on a trusted server to aggregate the spatio-temporal crowd-sourced data and then apply differential privacy to perturb the aggregate statistics before publishing to provide privacy guarantee. However, users' privacy will be exposed once the server is hacked or cannot be trusted. In this paper, we study the problem of real-time crowd-sourced statistical data publishing with strong privacy protection under an untrusted server. We propose a novel distributed agent-based privacy-preserving framework, called DADP, that introduces a new level of multiple agents between the users and the untrusted server. Instead of directly uploading data to the untrusted server, a user can randomly select one agent and upload the check-in information to it with the anonymous connection technology. Each agent aggregates the received crowd-sourced data and perturbs the aggregated statistics locally with Laplace mechanism. The perturbed statistics from all the agents are further combined together to form the entire perturbed statistics for publication. In particular, we propose a distributed budget allocation mechanism and an agent-based dynamic grouping mechanism to realize global w-event ε-differential privacy. Extensive experiments on real-world datasets demonstrate the effectiveness of DADP.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme.

Narrowband internet of things (NB-IoT) is a new cellular technology introduced by the Third Generation Partnership (3GPP) providing low-power and wide-area coverage for IoT. In this work, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (UE) to the serving base station (BS) cannot satisfy the quality of service (QoS) requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems and thus enables two-hop routes between NB-IoT user equipment (UE) and the serving BS via a set of D2D relays. As the candidate UE relays work in duty cycle to save energy, we derive a model to select a set of UE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

Honeypot technology can be applied to efficiently attract attackers and exhaust their resources. However, the traditional static honeypot is easy to be recognized by anti-honeypot technology. By contrast, most of the dynamic honeypots can simulate the real system in time, thus interacting with intruder in disguise. In this paper, we employ the dynamic property of honeypot in four kinds of services of our system. However, this dynamic property shows up in location and identification, indicating that the genuine or fake services (honeypots) are changeable in different hosts. Thus, the dynamic property of our system differs from the dynamic honeypot aforementioned. Besides, we adopt the blockchain platform (Ethereum) to decentralize our system and store the port access data by delivering a private chain. To illustrate the effectiveness of our scheme in theory and practice, security analysis, eavesdropping attack, scanning attack and DoS attack experiments are conducted. The results show that our scheme is valid in safeguarding against network attack.

Radio frequency identification (RFID), which promotes the rapid development of Internet of Things (IoT), has been an emerging technology and widely deployed in various applications such as warehouse management, supply chain management, and social networks. In such applications, objects can be efficiently managed by attaching them with low-cost RFID tags and carefully monitoring them. The missing objects, therefore, can be identified by the readers in the RFID system. Most of prior missing tag identification protocols consider the ideal scenario that all the tags' IDs are known to the reader, which ignore that some tags with unknown IDs, called unknown tags, may be present in the system. In this paper, we investigate the problem of efficiently identifying the missing tags with a predefined reliability for large-scale RFID systems with unknown tags. We first propose a basic efficient and reliable missing tag identification protocol called B-ERMI. Then we propose an enhanced protocol called E-ERMI to further improve the efficiency. The parameters of our proposed ERMI protocols are optimized to minimize the execution time. We also conduct extensive simulations to evaluate the proposed ERMI protocols and the simulation results illustrate that the ERMI protocols outperform other existing ones.