Efficient Anonymous Biometric Matching in Privacy-Aware Environments

Video surveillance is an important tool used in security and environmental monitoring, however, the widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. To identify these individuals for protection, the most reliable approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this dissertation, an Anonymous Biometric Access Control (ABAC) procedure is proposed based on biometric signals for privacy-aware video surveillance. The ABAC procedure uses Secure Multi-party Computational (SMC) based protocols to verify membership of an incoming individual without knowing his/her true identity. To make SMC-based protocols scalable to large biometric databases, I introduce the k-Anonymous Quantization (kAQ) framework to provide an effective and secure tradeoff of privacy and complexity. kAQ limits systems knowledge of the incoming individual to k maximally dissimilar candidates in the database, where k is a design parameter that controls the amount of complexity-privacy tradeoff. The relationship between biometric similarity and privacy is experimentally validated using a twin iris database. The effectiveness of the entire system is demonstrated based on a public iris biometric database. To provide the protected subjects with full access to their privacy information in video surveillance system, I develop a novel privacy information management system that allows subjects to access their information via the same biometric signals used for ABAC. The system is composed of two encrypted-domain protocols: the privacy information encryption protocol encrypts the original video records using the iris pattern acquired during ABAC procedure; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of my framework

Vulnerability anti-patterns:a timeless way to capture poor software practices (Vulnerabilities)

There is a distinct communication gap between the software engineering and cybersecurity communities when it comes to addressing reoccurring security problems, known as vulnerabilities. Many vulnerabilities are caused by software errors that are created by software developers. Insecure software development practices are common due to a variety of factors, which include inefficiencies within existing knowledge transfer mechanisms based on vulnerability databases (VDBs), software developers perceiving security as an afterthought, and lack of consideration of security as part of the software development lifecycle (SDLC). The resulting communication gap also prevents developers and security experts from successfully sharing essential security knowledge. The cybersecurity community makes their expert knowledge available in forms including vulnerability databases such as CAPEC and CWE, and pattern catalogues such as Security Patterns, Attack Patterns, and Software Fault Patterns. However, these sources are not effective at providing software developers with an understanding of how malicious hackers can exploit vulnerabilities in the software systems they create. As developers are familiar with pattern-based approaches, this paper proposes the use of Vulnerability Anti-Patterns (VAP) to transfer usable vulnerability knowledge to developers, bridging the communication gap between security experts and software developers. The primary contribution of this paper is twofold: (1) it proposes a new pattern template – Vulnerability Anti-Pattern – that uses anti-patterns rather than patterns to capture and communicate knowledge of existing vulnerabilities, and (2) it proposes a catalogue of Vulnerability Anti-Patterns (VAP) based on the most commonly occurring vulnerabilities that software developers can use to learn how malicious hackers can exploit errors in software

HardIDX: Practical and Secure Index with SGX

Software-based approaches for search over encrypted data are still either challenged by lack of proper, low-leakage encryption or slow performance. Existing hardware-based approaches do not scale well due to hardware limitations and software designs that are not specifically tailored to the hardware architecture, and are rarely well analyzed for their security (e.g., the impact of side channels). Additionally, existing hardware-based solutions often have a large code footprint in the trusted environment susceptible to software compromises. In this paper we present HardIDX: a hardware-based approach, leveraging Intel's SGX, for search over encrypted data. It implements only the security critical core, i.e., the search functionality, in the trusted environment and resorts to untrusted software for the remainder. HardIDX is deployable as a highly performant encrypted database index: it is logarithmic in the size of the index and searches are performed within a few milliseconds rather than seconds. We formally model and prove the security of our scheme showing that its leakage is equivalent to the best known searchable encryption schemes. Our implementation has a very small code and memory footprint yet still scales to virtually unlimited search index sizes, i.e., size is limited only by the general - non-secure - hardware resources

Using problem descriptions to represent variabilities for context-aware applications

This paper investigates the potential use of problem descriptions to represent and analyse variability in context-aware software products. By context-aware, we refer to recognition of changes in properties of external domains, which are recognised as affecting the behaviour of products. There are many reasons for changes in the operating environment, from fluctuating resources upon which the product relies, to different operating locations or the presence of objects. There is an increasing expectation for software intensivedevices to be context-aware which, in turn, adds further variability to problem description and analysis. However, we argue in this paper that the capture of contextual variability on current variability representations and analyses has yet to be explored. We illustrate the representation of this type of variability in a pilot study, and conclude with lessons learnt and an agenda for further work

Keys in the Clouds: Auditable Multi-device Access to Cryptographic Credentials

Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices. Storing keys in a centralized location, like an Internet-accessible server, raises serious security concerns (e.g. server compromise). Hardware-based Trusted Execution Environments (TEEs) are a well-known solution for protecting sensitive data in untrusted environments, and are now becoming available on commodity server platforms. Although the idea of protecting keys using a server-side TEE is straight-forward, in this paper we validate this approach and show that it enables new desirable functionality. We describe the design, implementation, and evaluation of a TEE-based Cloud Key Store (CKS), an online service for securely generating, storing, and using personal cryptographic keys. Using remote attestation, users receive strong assurance about the behaviour of the CKS, and can authenticate themselves using passwords while avoiding typical risks of password-based authentication like password theft or phishing. In addition, this design allows users to i) define policy-based access controls for keys; ii) delegate keys to other CKS users for a specified time and/or a limited number of uses; and iii) audit all key usages via a secure audit log. We have implemented a proof of concept CKS using Intel SGX and integrated this into GnuPG on Linux and OpenKeychain on Android. Our CKS implementation performs approximately 6,000 signature operations per second on a single desktop PC. The latency is in the same order of magnitude as using locally-stored keys, and 20x faster than smart cards.Comment: Extended version of a paper to appear in the 3rd Workshop on Security, Privacy, and Identity Management in the Cloud (SECPID) 201