Instant Privacy-Preserving Biometric Authentication for Hamming Distance

In recent years, there has been enormous research attention in privacy-preserving biometric authentication, which enables a user to verify him or herself to a server without disclosing raw biometric information. Since biometrics is irrevocable when exposed, it is very important to protect its privacy. In IEEE TIFS 2018, Zhou and Ren proposed a privacy-preserving user-centric biometric authentication scheme named PassBio, where the end-users encrypt their own templates, and the authentication server never sees the raw templates during the authentication phase. In their approach, it takes about 1 second to encrypt and compare 2000-bit templates based on Hamming distance on a laptop. However, this result is still far from practice because the size of templates used in commercialized products is much larger: according to NIST IREX IX report of 2018 which analyzed 46 iris recognition algorithms, size of their templates varies from 4,632-bit (579-byte) to 145,832-bit (18,229-byte). In this paper, we propose a new privacy-preserving user-centric biometric authentication (HDM-PPBA) based on Hamming distance, which shows a big improvement in efficiency to the previous works. It is based on our new single-key function-hiding inner product encryption, which encrypts and computes the Hamming distance of 145,832-bit binary in about 0.3 seconds on Intel Core i5 2.9GHz CPU. We show that it satisfies simulation-based security under the hardness assumption of Learning with Errors (LWE) problem. The storage requirements, bandwidth and time complexity of HDM-PPBA depend linearly on the bit-length of biometrics, and it is applicable to any large templates used in NIST IREX IX report with high efficiency

DeV-IP: A k-out-n Decentralized and verifiable BFV for Inner Product evaluation

The biometric system has become the desired alternative to a knowledge-based authentication system. An authentication system does not provide uniqueness, as a single user can create multiple registrations with different identities for authentication. Biometric authentication identifies users based on physical traits (fingerprint, iris, face, voice), which allows the system to detect multiple authentications from the same user. The biometric templates must be encrypted or hidden to preserve users\u27 privacy. Moreover, we need a system to perform the matching process over encrypted data without decrypting templates to preserve the users\u27 privacy. For the euclidean distance-based matching process, centralized server-based authentication leads to possible privacy violations of biometric templates since the power of computing inner product value over any two encrypted templates allows the server to retrieve the plain biometric template by computing a few inner products. To prevent this, we considered a decentralized system called collective authority, which is a part of a public network. The collective authority computes the collective public key with contributions from all nodes in the collective authority. It also performs a matching process over encrypted biometric templates in a decentralized manner where each node performs partial matching. Then the leader of the collective authority combines it to get the final value. We further provide a lattice-based verification system for each operation. Every time a node performs some computations, it needs to provide proof of the correctness of the computation, which is publicly verifiable. We finally make the system dynamics using Shamir\u27s secret sharing scheme. In dynamic collective authority, only $k$ nodes out of the total $n$ nodes are required to perform the matching process. We further show that the security of the proposed system relies on the security of the underlying encryption scheme and the secret sharing scheme

Efficient Verifiable Computation of XOR for Biometric Authentication

This work addresses the security and privacy issues in remotebiometric authentication by proposing an efficient mechanism to verifythe correctness of the outsourced computation in such protocols.In particular, we propose an efficient verifiable computation of XORingencrypted messages using an XOR linear message authenticationcode (MAC) and we employ the proposed scheme to build a biometricauthentication protocol. The proposed authentication protocol is bothsecure and privacy-preserving against malicious (as opposed to honest-but-curious) adversaries. Specifically, the use of the verifiable computation scheme together with an homomorphic encryption protects the privacy of biometric templates against malicious adversaries. Furthermore, in order to achieve unlinkability of authentication attempts, while keeping a low communication overhead, we show how to apply Oblivious RAM and biohashing to our protocol. We also provide a proof of security for the proposed solution. Our simulation results show that the proposed authentication protocol is efficient

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

Be More and be Merry: Enhancing Data and User Authentication in Collaborative Settings

Cryptography is the science and art of keeping information secret to un-intended parties. But, how can we determine who is an intended party and who is not? Authentication is the branch of cryptography that aims at confirming the source of data or at proving the identity of a person. This Ph.D. thesis is a study of different ways to perform cryptographic authentication of data and users. The main contributions are contained in the six papers included in this thesis and cover the following research areas: (i) homomorphic authentication; (ii) server-aided verification of signatures; (iii) distance-bounding authentication; and (iv) biometric authentication. The investigation flow is towards collaborative settings, that is, application scenarios where different and mutually distrustful entities work jointly for a common goal. The results presented in this thesis allow for secure and efficient authentication when more entities are involved, thus the title “be more and be merry”. Concretely, the first two papers in the collection are on homomorphic authenticators and provide an in-depth study on how to enhance existing primitives with multi- key functionalities. In particular, the papers extend homomorphic signatures and homomorphic message authentication codes to support computations on data authenticated using different secret keys. The third paper explores signer anonymity in the area of server-aided verification and provides new secure constructions. The fourth paper is in the area of distance-bounding authentication and describes a generic method to make existing protocols not only authenticate direct-neighbors, but also entities located two-hop away. The last two papers investigate the leakage of information that affects a special family of biometric authentication systems and how to combine verifiable computation techniques with biometric authentication in order to mitigate known attacks

On the Leakage of Fuzzy Matchers

In a biometric recognition system, the matcher compares an old and a fresh template to decide if it is a match or not. Beyond the binary output (`yes' or `no'), more information is computed. This paper provides an in-depth analysis of information leakage during distance evaluation, with an emphasis on threshold-based obfuscated distance (\textit{i.e.}, Fuzzy Matcher). Leakage can occur due to a malware infection or the use of a weakly privacy-preserving matcher, exemplified by side channel attacks or partially obfuscated designs. We provide an exhaustive catalog of information leakage scenarios as well as their impacts on the security concerning data privacy. Each of the scenarios leads to generic attacks whose impacts are expressed in terms of computational costs, hence allowing the establishment of upper bounds on the security level.Comment: Minor correction

Hybrid biometric template protection:Resolving the agony of choice between bloom filters and homomorphic encryption

Abstract Bloom filters (BFs) and homomorphic encryption (HE) are prominent techniques used to design biometric template protection (BTP) schemes that aim to protect sensitive biometric information during storage and biometric comparison. However, the pros and cons of BF‐ and HE‐based BTPs are not well studied in literature. We investigate the strengths and weaknesses of these two approaches since both seem promising from a theoretical viewpoint. Our key insight is to extend our theoretical investigation to cover the practical case of iris recognition on the ground that iris (1) benefits from the alignment‐free property of BFs and (2) induces huge computational burdens when implemented in the HE‐encrypted domain. BF‐based BTPs can be implemented to be either fast with high recognition accuracy while missing the important privacy property of ‘unlinkability’, or to be fast with unlinkability‐property while missing the high accuracy. HE‐based BTPs, on the other hand, are highly secure, achieve good accuracy, and meet the unlinkability‐property, but they are much slower than BF‐based approaches. As a synthesis, we propose a hybrid BTP scheme that combines the good properties of BFs and HE, ensuring unlinkability and high recognition accuracy, while being about seven times faster than the traditional HE‐based approach