Unlinkable and irreversible biometric template protection based on bloom filters

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Multi-biometric template protection based on bloom filters

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Deployments of biometric technologies are already widely disseminated, i.e. the protection of biometric reference data becomes vital in order to safeguard individuals' privacy. Biometric template protection techniques are designed to protect biometric templates in an irreversible and unlinkable manner (ISO/IEC IS 24745). In addition, these schemes are required to maintain key system properties, e.g. biometric performance or authentication speed. Recently, template protection schemes based on Bloom filters have been introduced and applied to various biometric characteristics, such as iris or face. While a Bloom filter-based representation of biometric templates is irreversible the originally proposed system has been exposed to be vulnerable to cross-matching attacks. In this paper we address this issue and demonstrate that any kind of Bloom filter-based representation of biometric templates can be transformed to an unordered set of integer values which enables a locking of irreversible templates in a fuzzy vault scheme from Dodis et al. which can be secured against known cross-matching attacks. In addition, experiments which are carried out on a publicly available iris database, show that the proposed scheme retains the biometric performance of the original system.

Security analysis and improvement of some biometric protected templates based on Bloom filters

Nowadays, biometric recognition represents an integral component of identity management and access control systems, replacing PINs or passwords. However, the wide deployment of biometric recognition systems in the past decades has raised numerous privacy concerns regarding the storage and use of biometric data. Due to the fact that the link between individuals and their biometric characteristics, e.g. fingerprints or iris, is strong and permanent, biometric reference data (templates) need to be protected in order to safeguard individuals' privacy and biometric systems' security. In particular, unprotected biometric templates can be abused to crossmatch biometric databases, i.e. tracking individuals without consent, and to launch presentation attacks employing specific inversion techniques. Technologies of biometric template protection offer solutions to privacy preserving biometric authentication, which improves the public confidence and acceptance of biometric systems. While biometric template protection has been an active research topic over the last 20 years, proposed solutions are still far from gaining practical acceptance. The existing gap between theory and practice, which results in a trade-off between privacy protection and recognition accuracy, is caused by numerous factors. To facilitate a standardized design and application of biometric template protection schemes, researchers have to solve several open problems. This chapter provides an overview of state-of-the-art technologies of biometric template protection and discusses main issues and challenges.

A lightweight machine learning-based authentication framework for smart IoT devices

Enforcing security and privacy guarantees for biometric users via protecting their unique and personalized attributes is an important area of research. There are many properties desirable in a biometric template protection scheme, but unfortunately, all of the requirements are not simultaneously fulfilled. In our work, we have tried to address this issue by proposing a cancelable framework for fingerprints which simultaneously provides satisfactory system performance, strong security guarantees, and fast matching procedure. The core of our scheme essentially pivots around the use of cryptographic hash functions which provide the adequate levels of security in the framework. Prior to the hashing stage, we have employed an effective pre-alignment technique and a hexagonal grid based quantization scheme which allows us to overcome constraint such as intra-class variability. Finally, our scheme is made cancelable through traditional salting. We tested our framework on various fingerprint databases and found that the resulting system performances were comparable with other contemporary cancelable schemes (EERs of 5.8%, 5.3%, 15.8%, 14.5% were observed for FVC2002 DB1, DB2, FVC2004 DB1, DB2 databases under the stolen token scenario). Most importantly, we perfectly fulfill the unlinkability, cancelability and diversity requirements, which are verified both theoretically and empirically.

In this correspondence, we analyze the vulnerabilities of biometric authentication protocols with respect to user and data privacy. The goal of an adversary in such context is not to bypass the authentication but to learn information either on biometric data or on users that are in the system. We elaborate our analysis on a general system model involving four logical entities (sensor, server, database, and matcher), and we focus on internal adversaries to encompass the situation where one or a combination of these entities would be malicious. Our goal is to emphasize that when going beyond the usual honest-but-curious assumption much more complex attacks can affect the privacy of data and users. On the one hand, we introduce a new comprehensive framework that encompasses the various schemes we want to look at. It presents a system model in which each internal entity or combination of entities is a potential attacker. Different attack goals are considered and resulting requirements on data flows are discussed. On the other hand, we develop different generic attacks. We follow a blackbox approach in which we consider components that perform operations on biometric data but where only the input/output behavior is analyzed. These attack strategies are exhibited on recent schemes such as the distributed protocol of Bringer (ACISP 2007), which is based on the Goldwasser-Micali cryptosystem, the related protocol of Barbosa (ACISP 2008), which uses the Paillier cryptosystem, and the scheme of Stoianov (SPIE 2010), that features the Blum-Goldwasser cryptosystem. All these schemes have been developed in the honest-but-curious adversary model and show potential weaknesses when considered in our malicious insider attack model.

Compared with a hash table, a Bloom Filter (BF) is more space-efficient for supporting fast matching though resulting in a controllable and acceptable false positive probability. The space size of the basic BF is predetermined based on the expected number of elements to be stored. However, we cannot predict the scale of a BF space for dynamic sets. The two existing solutions for supporting dynamic sets, Scalable BF (SBF) and Dynamic BF (DBF), still face some challenges on system performance and memory overhead.This paper presents a new BF for dynamic data sets, called Partitioned BF (Par-BF). Compared with DBF and SBF, the size and the range of the false positive probability can be calculated by a group of formulas to leverage a sweet spot between high-performance and low-overhead. Moreover, Par-BF supports parallel fast matching which can improve the overall throughput. From our trace-driven experimental results, the IOPS of Par-BF outperforms that of DBF and SBF from 6X to 10X, and from 2X to 4X, respectively. Meanwhile, through our proposed garbage collection policy, the memory overhead of Par-BF is less than half of the memory usage of SBF.

Bloom filters are widely used in many network applications but the high computation cost limits the system performance. In this paper, we introduce a new variation of Bloom filter named One-Hashing Bloom Filter (OHBF) to solve the problem. OHBF requires only one base hash function plus a few simple operations to implement a Bloom filter. While keeping nearly the same theoretical false positive ratio as an ideal Bloom filter, OHBF significantly reduces the hash computation overhead. We show that the false positive performance of a standard Bloom filter implementation strongly relies on the selection of hash functions, even if these hash functions are considered good. In contrast, OHBF presents consistently better performance with a proven mathematical foundation. OHBF is ideal for high throughput and low latency applications. As OHBF is a fundamental technique in Bloom filter theory, it can be applied to many other Bloom filter variations, such as Counting Bloom Filter and Space-Code Bloom Filter.

Bloom filters are probabilistic data structures that are popular in networking for set representation; however, they show an inherent inaccuracy due to false positives. One of the potential attacks on Bloom filters is to pollute them with elements that cause the filter to have a larger false positive probability than under normal operation; Pollution is simple when an attacker knows the details of the filter implementation. Recent research has shown that also black-box adversaries can pollute a counting Bloom filter (a common variant of the filter that also supports removals) with no knowledge of its implementation. As over time, many variants and improvements of Bloom filters have been proposed, it is of interest to study whether they can also be polluted and if so also the increase in their false positive probability. This paper first proposes and then evaluates pollution attacks for some of the most common variants including the Block Bloom filters (BBFs), the Variable Increment and Fingerprint Counting Bloom filters (VI-CBFs and FP-CBFs). The results show that with or without knowledge of the implementation, these variants of the Bloom filter are significantly more vulnerable to pollution attacks than the traditional Bloom filter. In particular, BBFs are extremely vulnerable, so providing an insight on their impact and use in practical systems when the number of memory accesses per lookup must be reduced.

The security of information and personal privacy are the growing concerns in today’s human life worldwide. The storage of biometric data in the database has raised the prospect of compromising the database leading to grave risks and misuse of the person’s privacy such as growth in terrorism and identity fraud. When a person’s biometric data stored is revealed, their security and privacy are being compromised. This research described a detailed evaluation on several outbreaks and threats associated with the biometric technology. It analyzed the user’s fear and intimidations to the biometric technology alongside the protection steps for securing the biometric data template in the database. It is known that, when somebody’s biometric data template is compromised from the database that consequently might indicate proof of identity robbery of that person. Mixed method to compute and articulate the results as well as a new tactic of encryption-decryption algorithm with a design pattern of Model View Template (MVT) are used for securing the biometric data template in the database. The model managed information logically, the view indicated the visualization of the data, and the template directed the data migration into pattern object. Factors influencing fear of biometric technology such as an exposer of personal information, improper data transfer, and data misuse are found. Strong knowledge of the ideal technology like the private skills of the biometric technology, data secrecy and perceived helpfulness are established. The fears and attacks along the technology like a counterfeit of documents and brute-force attack are known. The designed algorithm based on the cryptographic module of the Fernet keys instance are utilized. The Fernet keys are combined to generate a multiFernet key, integrated with biometric data to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and firmly stored in the database. The storage database has security measures that guard against an impostor’s attack. The database system can block the attacker from unauthorized access. Thus, significantly increased individual data privacy and integrity.

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.

The authors of this work claim that the legislation of the European Union does not adequately protect biometric data, due to the actuality that the protection of biometric data after the death of a person is unresolved within the legal field of personal data protection. The General Data Protection Regulation (GDPR) does not apply to deceased individuals, meaning that biometric data that was processed under the GDPR during the individual's lifetime continues to be at risk of re-processing after the individual's death, meaning that businesses can potentially avoid GDPR when the person has died, and decide the fate of the deceased's biometrics at their own discretion. Taking into account the above, the authors of the work consider it necessary to orient special legislation to overcome the specified legal gap, and since the regulation on the protection of personal data is vague to solve the problem, the research proposal of the authors consists in the application of legal guarantees not only under the GDPR but also in conformity with civil law. Therefore, the authors aim to initiate provisions for potential legislation, including the consolidation of the processing of biometric data of the deceased as part of a person's last will. The development of legal norms that would protect the biometric data of deceased persons by classifying them as non-property rights is especially recommended for countries with a civil law system. The research paper also warns that privileges that belong to the non-property rights of an individual do not, as a rule, automatically pass to heirs. In this regard, in the absence of a person's will to decide the future fate of biometric data that is continuously processed at the time or after death, the authors offer to allow family members and other close persons to exercise the right to the protection of personal data, such as the right to be forgotten, on behalf of the deceased, and to demand the condition of appropriate technical and organizational guarantees in the event of such performance. In this matter, according to the authors, it is expedient to conclude agreements between interested parties and family members to determine the fate of biometric data. However, in order to avoid the realization of the right to the protection of personal data, which the deceased did not have time to realize during his lifetime, and if during the life of the person Article, 9 (2, a) GDPR confirmed as the basis for the permitted biometric data processing because it is subject to the consent of the person, then following the human-centric approach of the European Union, it is proposed to design and indicate in such consent a clause where the individual could specifically determine and decide the fate of the biometric data upon death, for example, to (i) delete, (ii) transfer for the research purposes. Consequently, the authors predict the development of legal regulation of biometric data by assigning it to the category of non-property rights. Hence, the adoption of special provisions for the protection of the deceased biometric data and ensuring the realization of the right to personal data protection is expected not only during life but also after death.