Towards standardizing trusted evidence of identity

Abstract

Evidence of identity (EOI), sometimes mentioned as breeder documents in a physical representation form, refers to a single or a set of evidence that can be used to provide confidence to the claimed identity. Trust of evidence of identity needs prudential assessment by an authority or a service provider before such evidence of identity can be accepted for identity verification or eligibility evaluation purposes. This is especially the case when such purposes have a high risk of cost, security, and other critical consequences. In this paper we analyze the status of deployed EOIs in the scope of ePassport issuance; and then attempt to define the implementation types, fraud scenarios, security objectives, and trust levels for EOIs, which have not been clearly defined in existing research and standardization societies. As a pilot survey work in this field, recommendations from policy and technology perspectives towards a highly-trusted EOI framework for standardization are shaped towards future ePassport issuance standardization and practice. Finally, a new design of birth certificate, compliant to the proposed recommendations, is presented as an example of trusted EOI.

A Survey on Multimodal Biometrics and the Protection of Their Templates

“…In this work, we have presented a concrete approach on the protection of multimodal biometric templates, underlying critical privacy issues, while focusing on the suggestions for future research. Multimodal biometric systems are mostly discussed for the impact of their use on publicly accepted, reliable identification systems [31], [53], overcoming the obstacles of uni-modal ones.…”

“…In addition to the idea that biometric characteristics are only useful in forensics, the pronounced necessity for reliable day-to-day transactions, has led to a range of applications that verify the identity of a person using human properties. Systems are increasingly being deployed and used throughout the world [53], from limited simple home or business applications (the controlled access to a room), to large-scale projects, which are involved in societal functions, such as user verification for on-line transactions (e.g. banking ePayments, mobile devices).…”

Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support

“…For security checkpoints, the source reliability and information credibility are represented by the probabilistic variables such as false ID, multiple ID of the same person, and features of intentional data alteration in the chip (e.g., biometric traits and text data), as well as a false life-cycle history [11] , [49] .…”

Cognitive Identity Management: Synthetic Data, Risk and Trust

“…Let us assume that in training algorithms for validation of ID (node V ) and identification of ID source reliability (node R), synthetic data was used to represent rare events such as false ID, multiple ID of the same person, and features of intentional data alteration in the chip (e.g. biometric traits and text data) as well as a false life cycle history [50]. Probabilities of these threats are represented in Conditional Probabilities Tables (CPTs) for nodes V and R: P (V = v 2 ) = 0.01 and P (R = r 3 ) = 0.2 ≡ low (Fig.…”