Visible to the public Biblio

Filters: Author is Yu, J.  [Clear All Filters]
2016
Li, H., He, Y., Sun, L., Cheng, X., Yu, J..  2016.  Side-channel information leakage of encrypted video stream in video surveillance systems. IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications. :1–9.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

2017
Zhang, P., Zhang, X., Sun, X., Liu, J. K., Yu, J., Jiang, Z. L..  2017.  Anonymous Anti-Sybil Attack Protocol for Mobile Healthcare Networks Analytics. 2017 IEEE Trustcom/BigDataSE/ICESS. :668–674.

Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.

Li, W., Song, T., Li, Y., Ma, L., Yu, J., Cheng, X..  2017.  A Hierarchical Game Framework for Data Privacy Preservation in Context-Aware IoT Applications. 2017 IEEE Symposium on Privacy-Aware Computing (PAC). :176–177.

Due to the increasing concerns of securing private information, context-aware Internet of Things (IoT) applications are in dire need of supporting data privacy preservation for users. In the past years, game theory has been widely applied to design secure and privacy-preserving protocols for users to counter various attacks, and most of the existing work is based on a two-player game model, i.e., a user/defender-attacker game. In this paper, we consider a more practical scenario which involves three players: a user, an attacker, and a service provider, and such a complicated system renders any two-player model inapplicable. To capture the complex interactions between the service provider, the user, and the attacker, we propose a hierarchical two-layer three-player game framework. Finally, we carry out a comprehensive numerical study to validate our proposed game framework and theoretical analysis.

2018
Yu, J., Ding, F., Zhao, X., Wang, Y..  2018.  An Resilient Cloud Architecture for Mission Assurance. 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC). :343–346.
In view of the demand for the continuous guarantee capability of the information system in the diversified task and the complex cyber threat environment, a dual loop architecture of the resilient cloud environment for mission assurance is proposed. Firstly, general technical architecture of cloud environment is briefly introduced. Drawing on the idea of software definition, a resilient dual loop architecture based on "perception analysis planning adjustment" is constructed. Then, the core mission assurance system deployment mechanism is designed using the idea of distributed control. Finally, the core mission assurance system is designed in detail, which is consisted of six functional modules, including mission and environment awareness network, intelligent anomaly analysis and prediction, mission and resource situation generation, mission and resource planning, adaptive optimization and adjustment. The design of the dual loop architecture of the resilient cloud environment for mission assurance will further enhance the fast adaptability of the information system in the complex cyber physical environment.
Lu, L., Yu, J., Chen, Y., Liu, H., Zhu, Y., Liu, Y., Li, M..  2018.  LipPass: Lip Reading-based User Authentication on Smartphones Leveraging Acoustic Signals. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1466–1474.

To prevent users' privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, etc., to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Towards this end, we explore liveness verification of user authentication leveraging users' lip movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users' speaking lips leveraging build-in audio devices on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking lips, and find that there are unique lip movement patterns for different individuals. To characterize the lip movements, we propose a deep learning-based method to extract efficient features from Doppler profiles, and employ Support Vector Machine and Support Vector Domain Description to construct binary classifiers and spoofer detectors for user identification and spoofer detection, respectively. Afterwards, we develop a binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.21% accuracy in user identification and 93.1% accuracy in spoofer detection.