A Security Measure for Binary-Template-based Biometric System

Project Team: Prof. YUEN, Pong Chi

Biometrics is irrevocable and the effect of compromise is permanent and irrecoverable. The biometric data is often transformed into binary to allow such binary biometric representation to be protected. However, biometric data are susceptible to intra-user variations. Thus, each user is represented by multiple similar binary strings. To ensure reasonable acceptance rate of genuine queries, binary-representation-based systems are designed to accept multiple similar codewords to the enrolled template, where the acceptable similarity is parameterized by a system parameter τ. In this project, we work towards developing a measuring model to quantify the unpredictability of system output.

Biometric Indexing and Identity De-duplication

Project Team: Prof. CHEUNG, Yiu Ming, Prof. YUEN, Pong Chi and Dr. WANG, Yi

In vast biometric data collections, identity de-duplication is essential to ensure the uniquess of every enrolled biometric records. Recently, there are discussions to leverage the cloud for mitigating the computation burden of identity de-duplication from the central system to distributed processing units. However, it is neither feasible nor secure to make multiple copies of biometric templates at distributed sites. Some hash-like index codes are needed to form surrogates of the biometric database. This project aims to propose compact index codes and novel multi-biometric indexing schemes for effective and efficient identity de-duplication.

iGPS: Privacy-Preserving Geo-Proximity Services in Location-based Social Networks

Project Team: Prof. XU, Jian Liang

A geo-proximity service in location-based social networks alerts a mobile user when any of his/her friends is in the geographical vicinity, so as to enrich social activities such as collaborative working and information sharing. To realize such services, existing systems collect location information from mobile users for proximity computation, which raises serious privacy concerns. This project aims to develop more sophisticated location update and query techniques that support these geo-proximity services while preserving the location privacy of mobile users.

Lip-password for Personal Identity Verification

Project Team: Prof. CHEUNG, Yiu Ming

This project presents a lip-password based personal identity verification (PIV) approach, which combines the merits of lip-motion characteristics and the password information. This new technique features a double security to the PIV upon that a personal identity is verified by both of the underlying behavior characteristics of lip motions and the password information simultaneously. Subsequently, a target person saying the wrong password or an impostor even knowing the correct password will be detected successfully. It is anticipated that PIV based on lip-password can meet the security requirement of different fields, such as financial transaction, secure access, human-computer interface, and so on.

Modeling Spatial Relationships in Human and Object Interactions for Human Activity Understanding in Video Surveillance

Project Team: Prof. YUEN, Pong Chi

With the rapid growth in the amount of surveillance video captured by CCTV cameras, analyzing scene in the data automatically becomes more and more important. However, most of the existing approaches in human activity understanding focus on analyzing the movement of the human subject being tracked only. On the other hand, less attention has been paid on extracting context in human-object interactions to provide addition information for analysis. In this project, we will develop a view-insensitive spatial relation based model to represent human-object interactions. Preliminary results show that the prototype of our new representation is robust in different situations.

Multimodal Biometric Template Protection

Project Team: Prof. YUEN, Pong Chi

Multimodal biometric recognition offers higher performance accuracy than uni-modal recognition due to higher number of distinctive biometric features that can be leveraged in constructing a more discriminative feature-set representation for recognition. As a result, multimodal template protection appears to be more crucial as the consequence of compromise of multi-modal template will be far more devastating than that of the uni-modal template. To alleviate this threat, this project aims to design, analyze, and develop a multimodal biometric template protection scheme for optimal preservation of discrimination power of biometric representation, user security and privacy.

Privacy-Conscious Query Authentication for Outsourced and Cloud Databases

Project Team: Prof. XU, Jian Liang

In data oursourcing, it is critical for a query client to be able to authenticate the result of a query from the outsourced server, in terms of both soundness and completeness. However, existing works assume that during the authentication process, the client can always be trusted and entitled to receive data values on the querying attribute(s), even if they are not the results. This severely jeopardizes the privacy of the data owner. In this project, we study authentication for such a privacy-conscious query model where the querying attribute(s) are unavailable to the client.

Private Sharing of Heterogeneous Health Data

Project Team: Prof. XU, Jian Liang

Individual privacy is of paramount importance in the sharing of health data. In spite of the extensive research on privacy-preserving data publishing, existing works, however, are challenged by health data’s inherent heterogeneity. In this project, we aim to conduct the first thorough investigation of privacy implications in sharing heterogeneous health data and consequently develop trustworthy interactive and non-interactive mechanisms to provide provable privacy guarantees, without requiring privacy expertise from patients or medical practitioners, while effectively supporting the diverse use cases on health data.

Spatio-Temporal Attestation for Location-based Services Using Private Signatures

Project Team: Prof. XU, Jian Liang

In location-based services, there has been a growing necessity against location cheating and for location trustworthiness. In this project, we propose "spatio-temporal attestation", where a mobile user testifies or attests to a service provider the genuineness of his/her input location against some spatio-temporal predicate, such as "being in a specific region during a time period". The major challenge is the need of protecting location privacy of mobile users against the service provider during the attestation process.