Zhaoyu Gao

A Probabilistic Misbehavior Detection Scheme toward Efficient Trust Establishment in Delay-Tolerant Networks

Malicious and selfish behaviors represent a serious threat against routing in delay/disruption tolerant networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN is regarded as a great challenge. In this paper, we propose iTrust, a probabilistic misbehavior detection scheme, for secure DTN routing toward efficient trust establishment. The basic idea of iTrust is introducing a periodically available Trusted Authority (TA) to judge the nodes behavior based on the collected routing evidences and probabilistically checking. We model iTrust as the inspection game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a nodes reputation, which allows a dynamic detection probability determined by the trust of the users. The extensive analysis and simulation results demonstrate the effectiveness and efficiency of the proposed scheme.

Security and privacy of collaborative spectrum sensing in cognitive radio networks

Collaborative spectrum sensing is regarded as a promising approach to significantly improve the performance of spectrum sensing in cognitive radio networks. However, due to the open nature of wireless communications and the increasingly available software defined radio platforms, collaborative spectrum sensing also poses many new research challenges, especially in the aspect of security and privacy. In this article, we first identify the potential security threats toward collaborative spectrum sensing in CRNs. Then we review the existing proposals related to secure collaborative spectrum sensing. Furthermore, we identify several new location privacy related attacks in collaborative sensing, which are expected to compromise secondary users¿ location privacy by correlating their sensing reports and their physical location. To thwart these attacks, we propose a novel privacy preserving framework in collaborative spectrum sensing to prevent location privacy leaking. We design and implement a real-world testbed to evaluate the system performance. The attack experiment results show that if there is no any security guarantee, the attackers could successfully compromise a secondary user¿s location privacy at a success rate of more than 90 percent. We also show that the proposed privacy preserving framework could significantly improve the location privacy of secondary users with a minimal effect on the performance of collaborative sensing.

Location privacy preservation in collaborative spectrum sensing

Collaborative spectrum sensing has been regarded as a promising approach to enable secondary users to detect primary users by exploiting spatial diversity. In this paper, we consider a converse question: could space diversity be exploited by a malicious entity, e.g., an external attacker or an untrusted Fusion Center (FC), to achieve involuntary geolocation of a secondary user by linking his location-dependent sensing report to his physical position. We answer this question by identifying a new security threat in collaborative sensing from testbed implementation, and it is shown that the attackers could geo-locate a secondary user from its sensing report with a successful rate of above 90% even in the presence of data aggregation. We then introduce a novel location privacy definition to quantify the location privacy leaking in collaborative sensing. We propose a Privacy Preserving collaborative Spectrum Sensing (PPSS) scheme, which includes two primitive protocols: Privacy Preserving Sensing Report Aggregation protocol (PPSRA) and Distributed Dummy Report Injection Protocol (DDRI). Specifically, PPSRA scheme utilizes applied cryptographic techniques to allow the FC to obtain the aggregated result from various secondary users without learning each individuals values while DDRI algorithm can provide differential location privacy for secondary users by introducing a novel sensing data randomization technique. We implement and evaluate the PPSS scheme in a real-world testbed. The evaluation results show that PPSS can significantly improve the secondary users location privacy with a reasonable security overhead in collaborative sensing.

Fairness-Aware and Privacy-Preserving Friend Matching Protocol in Mobile Social Networks

Mobile social networks represent a promising cyber-physical system, which connects mobile nodes within a local physical proximity using mobile smart phones as well as wireless communication. In mobile social networks, the mobile users may, however, face the risk of leaking their personal information and location privacy. In this paper, we first model the secure friend discovery process as a generalized privacy-preserving interest and profile matching problem. We identify a new security threat arising from existing secure friend discovery protocols, coined as runaway attack, which can introduce a serious unfairness issue. To thwart this new threat, we introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and transformed results. Based on this, we propose our privacy-preserving and fairness-aware interest and profile matching protocol, which allows one party to match its interest with the profile of another, without revealing its real interest and profile and vice versa. The detailed security analysis as well as real-world implementations demonstrate the effectiveness and efficiency of the proposed protocol.

YouSense: Mitigating entropy selfishness in distributed collaborative spectrum sensing

Collaborative spectrum sensing has been recognized as a promising approach to improve the sensing performance via exploiting the spatial diversity of the secondary users. In this study, a new selfishness issue is identified, that selfish users sense no spectrum in collaborative sensing. For easier presentation, its denoted as entropy selfishness. This selfish behavior is difficult to distinguish, making existing detection based incentive schemes fail to work. To thwart entropy selfishness in distributed collaborative sensing, we propose YouSense, a One-Time Pad (OTP) based incentive design that could naturally isolate entropy selfish users from the honest users without selfish node detection. The basic idea of YouSense is to construct a trapdoor onetime pad for each sensing report by combining the original report and a random key. Such a one-time pad based encryption could prevent entropy selfish users from accessing the original sensing report while enabling the honest users to recover the report. Different from traditional cryptography based OTP which requires the key delivery, YouSense allows an honest user to recover the pad (or key) by exploiting a unique characteristic of collaborative sensing that different secondary users share some common observations on the same radio spectrum. We further extend YouSense to improve the recovery successful rate by reducing the cardinality of set of the possible pads. By extensive USRP based experiments, we show that YouSense can successfully thwart entropy selfishness with low system overhead.

PMDS: A probabilistic misbehavior detection scheme in DTN

Malicious and selfish behaviors represent a serious threat against routing in Delay or Disruption Tolerant Networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN represents a great challenge. In this paper, we propose PMDS, a probabilistic misbehavior detection scheme, for secure DTN routing. The basic idea of PMDS is introducing a periodically available Trusted Authority (TA), which judges the nodes behavior based on the collected routing evidences. We model PMDS as the Inspection Game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a nodes reputation, which allows a dynamic detection probability determined by a nodes reputation. The extensive analysis and simulation results show that the proposed scheme substantiates the effectiveness and efficiency of the proposed scheme.

Location privacy leaking from spectrum utilization information in database-driven cognitive radio network

The Database-driven Cognitive Radio Network is regarded as a promising way for a better utilization of radio channels without introducing the interference to the primary user. However, it is also facing a series of security threats. In this study, we identify a new kind of location privacy related attack which could geo-locate a secondary user from the spectrum he used. We propose a Spectrum Utilization based Location Inference Algorithm, which is based on the intersection of the possible location sets revealed by each channel access or channel transition event under the presence of the primary user. We implement our algorithm on the data extracted from Google Earth Coverage Maps released by FCC. Our experiement results show that,

PriMatch: Fairness-aware secure friend discovery protocol in mobile social network

80\%

Towards addressing group selfishness of cluster-based collaborative spectrum sensing in cognitive radio networks

SUs could be located to 10 cells based on 25 or less channels.

All your location are belong to us: breaking mobile social networks for automated user location tracking

Mobile social networks are expected to substantially enrich interaction with ubiquitous computing environments by integrating social context information into local interactions. However, in mobile social networks, the mobile users may face the risk of leaking their personal information and their location privacy. In this study, we first model the secure friend discovery process as a generalized privacy-preserving interest/profile matching problem. Then, we identify a new security threat arising from existing secure friend discovery protocols, coined as runaway attack, which is expected to introduce serious fairness issue. To address this new threat, we introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and the transformed result. Based on it, we propose our fairness-aware privacy preserving interest/profile matching protocol, which enables one party to match its interest with the profile of another, without revealing its real interest and profile and vice versa. The detailed security analysis as well as real-world implementations demonstrate the effectiveness and the efficiency of the proposed protocol.