Xukai Zou

Permanent URI for this collection

https://hdl.handle.net/1805/26681

Creating Secure and Private-Preserving Cyber Infrastructure

The project is the first attempt to build a secure, holistic, and resilient cybersecurity architecture for any computing systems so that different types of users can remotely access and share protected data/resource/workflow in a free, flexible, yet finely-controlled, manner. The developed secure infrastructure will provide multi-level comprehensive protection from user authentication to fine-tuned data access, to confidentiality, integrity, availability, and traceability. The developed secure architecture is based on cutting-edge and advanced security technologies most of which have been invented or designed by Professor Zou and his team of researchers. The secure architecture can be applied to any multi-user and dynamic data/resource sharing systems and cyber infrastructures such as scientific infrastructures, health care systems, power-grid infrastructures, law-enforcement and forensic systems, and secure smart-city and smart-home infrastructures to protect the systems or infrastructures from both internal and external attacks.

Professor Zou's translation of research into secure, online transactions and interactions is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.

Browse

Now showing 1 - 10 of 25

AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization
(IEEE, 2020-10) Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei; BioHealth Informatics, School of Informatics and Computing
In this paper, we propose a novel, privacy-preserving, and integrated authentication and authorization scheme (dubbed as AuthN-AuthZ). The proposed scheme can address both the usability and privacy issues often posed by authentication through use of privacy-preserving Biometric-Capsule-based authentication. Each Biometric-Capsule encapsulates a user's biometric template as well as their role within a hierarchical Role-based Access Control model. As a result, AuthN-AuthZ provides novel efficiency by performing both authentication and authorization simultaneously in a single operation. To the best of our knowledge, our scheme's integrated AuthN-AuthZ operation is the first of its kind. The proposed scheme is flexible in design and allows for the secure use of robust deep learning techniques, such as the recently proposed and current state-of-the-art facial feature representation method, ArcFace. We conduct extensive experiments to demonstrate the robust performance of the proposed scheme and its AuthN-AuthZ operation.
A Cancellable and Privacy-Preserving Facial Biometric Authentication Scheme
(IEEE, 2017) Phillips, Tyler; Zou, Xukai; Li, Feng; Computer and Information Science, School of Science
In recent years, biometric, or "who you are," authentication has grown rapidly in acceptance and use. Biometric authentication offers users the convenience of not having to carry a password, PIN, smartcard, etc. Instead, users will use their inherent biometric traits for authentication and, as a result, risk their biometric information being stolen. The security of users' biometric information is of critical importance within a biometric authentication scheme as compromised data can reveal sensitive information: race, gender, illness, etc. A cancellable biometric scheme, the "BioCapsule" scheme, proposed by researchers from Indiana University Purdue University Indianapolis, aims to mask users' biometric information and preserve users' privacy. The BioCapsule scheme can be easily embedded into existing biometric authentication systems, and it has been shown to preserve user-privacy, be resistant to several types of attacks, and have minimal effects on biometric authentication system accuracy. In this research we present a facial authentication system which employs several cutting-edge techniques. We tested our proposed system on several face databases, both with and without the BioCapsule scheme being embedded into our system. By comparing our results, we quantify the effects the BioCapsule scheme, and its security benefits, have on the accuracy of our facial authentication system.
Dependability and Security in Medical Information System
(Springer Nature, 2007) Zou, Xukai; Dai, Yuan-Shun; Doebbeling, Bradley; Qi, Mingrui; Department of Computer and Information Science, School of Science
Medical Information Systems (MIS) help medical practice and health care significantly. Security and dependability are two increasingly important factors for MIS nowadays. In one hand, people would be willing to step into the MIS age only when their privacy and integrity can be protected and guaranteed with MIS systems. On the other hand, only secure and reliable MIS systems would provide safe and solid medical and health care service to people. In this paper, we discuss some new security and reliability technologies which are necessary for and can be integrated with existing MISs and make the systems highly secure and dependable. We also present an implemented Middleware architecture which has been integrated with the existing VISTA/CPRS system in the U.S. Department of Veterans Affairs seamlessly and transparently.
Design and Implementation of Privacy-Preserving, Flexible and Scalable Role-Based Hierarchical Access Control
(IEEE, 2019-12) Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Zou, Xukai; Computer and Information Science, School of Science
In many domains, organizations must model personnel and corresponding data access privileges as fine-grained hierarchical access control models. One class of such models, Role-based Access Control (RBAC) models, has been widely accepted and deployed. However, RBAC models are often used without involving cryptographic keys nor considering confidentiality/privacy at the data level. How to design, implement and dynamically modify such a hierarchy, ensure user and data privacy and distribute and manage necessary cryptographic keys are issues of the utmost importance. One elegant solution for cryptography-based hierarchical access control combines the collusion-resistant and privacy-preserving Access Control Polynomial (ACP) and Atallah's Dynamic and Efficient Extended Key Management scheme. Such a model involves cryptographic keys used to encrypt data, can address confidentiality/privacy at the data level and can efficiently support dynamic changes to the RBAC access hierarchy. In this paper, we discuss several implementation challenges and propose solutions when deploying such a system including: data encryption and decryption, key storage and key distribution. Furthermore, we provide analysis of the efficiency and scalability of the resulting system.
Electronic Voting Technology Inspired Interactive Teaching and Learning Pedagogy and Curriculum Development for Cybersecurity Education
(Springer, 2021-07) Hosler, Ryan; Zou, Xukai; Bishop, Matt; Computer and Information Science, School of Science
Cybersecurity is becoming increasingly important to individuals and society alike. However, due to its theoretical and practical complexity, keeping students interested in the foundations of cybersecurity is a challenge. One way to excite such interest is to tie it to current events, for example elections. Elections are important to both individuals and society, and typically dominate much of the news before and during the election. We are developing a curriculum based on elections and, in particular, an electronic voting protocol. Basing the curriculum on an electronic voting framework allows one to teach critical cybersecurity concepts such as authentication, privacy, secrecy, access control, encryption, and the role of non-technical factors such as policies and laws in cybersecurity, which must include societal and human factors. Student-centered interactions and projects allow them to apply the concepts, thereby reinforcing their learning.
Energy-Efficient Device Selection in Federated Edge Learning
(IEEE, 2021-07) Peng, Cheng; Hu, Qin; Chen, Jianan; Kang, Kyubyung; Li, Feng; Zou, Xukai; Computer and Information Science, School of Science
Due to the increasing demand from mobile devices for the real-time response of cloud computing services, federated edge learning (FEL) emerges as a new computing paradigm, which utilizes edge devices to achieve efficient machine learning while protecting their data privacy. Implementing efficient FEL suffers from the challenges of devices’ limited computing and communication resources, as well as unevenly distributed datasets, which inspires several existing research focusing on device selection to optimize time consumption and data diversity. However, these studies fail to consider the energy consumption of edge devices given their limited power supply, which can seriously affect the cost-efficiency of FEL with unexpected device dropouts. To fill this gap, we propose a device selection model capturing both energy consumption and data diversity optimization, under the constraints of time consumption and training data amount. Then we solve the optimization problem by reformulating the original model and designing a novel algorithm, named E2DS, to reduce the time complexity greatly. By comparing with two classical FEL schemes, we validate the superiority of our proposed device selection mechanism for FEL with extensive experimental results.
Enhancing and Implementing Fully Transparent Internet Voting
(IEEE, 2015-08) Butterfield, Kevin; Li, Huian; Zou, Xukai; Li, Feng; Department of Computer & Information Science, School of Science
Voting over the internet has been the focus of significant research with the potential to solve many problems. Current implementations typically suffer from a lack of transparency, where the connection between vote casting and result tallying is seen as a black box by voters. A new protocol was recently proposed that allows full transparency, never obfuscating any step of the process, and splits authority between mutually-constraining conflicting parties. Achieving such transparency brings with it challenging issues. In this paper we propose an efficient algorithm for generating unique, anonymous identifiers (voting locations) that is based on the Chinese Remainder Theorem, we extend the functionality of an election to allow for races with multiple winners, and we introduce a prototype of this voting system implemented as a multiplatform web application.
Enhancing and Implementing Fully Transparent Internet Voting
(Office of the Vice Chancellor for Research, 2015-04-17) Butterfield, Kevin; Li, Huian; Zou, Xukai; Li, Feng
Voting over the internet has been the focus of significant research with the potential to solve many problems. Current implementations typically suffer from a lack of transparency, where the connection between vote casting and result tallying is seen as a black box by voters. A new protocol was recently proposed that allows full transparency, never obfuscating any step of the process, and splits authority between mutually-constraining conflicting parties. Achieving such transparency brings with it challenging issues. In this paper we propose an efficient algorithm for generating unique, anonymous identifiers (voting locations) that is based on the Chinese Remainder Theorem, extend the functionality of an election to allow for races with multiple winners, and introduce a prototype of this voting system implemented as a multiplatform web application.
Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning
(ACM, 2019) Phillips, Tyler; Zou, Xukai; Li, Feng; Li, Ninghui; Computer and Information Science, School of Science
In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques.
Hardware Speculation Vulnerabilities and Mitigations
(IEEE, 2021-10) Swearingen, Nathan; Hosler, Ryan; Zou, Xukai; Computer and Information Science, School of Science
This paper will discuss speculation vulnerabilities, which arise from hardware speculation, an optimization technique. Unlike many other types of vulnerabilities, these are very difficult to patch completely, and there are techniques developed to mitigate them. We will look at many of the variants of this type of vulnerability. We will look at the techniques mitigating those vulnerabilities and the effectiveness and scope of each. Finally, we will compare and evaluate different vulnerabilities and mitigation techniques and recommend how various mitigation techniques apply to different situations.

Browse

Browsing Xukai Zou by Title

Results Per Page

Sort Options