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1. Multi random projection inner product encryption, applications to proximity searchable encryption for the iris biometric

   https://doi.org/10.1016/j.ic.2023.105059  

   Cachet, Chloe; Ahmad, Sohaib; Demarest, Luke; Riback, Serena; Hamlin, Ariel; Fuller, Benjamin (August 2023, Information and Computation)

   Biometric databases collect people's information and perform proximity search (finding records within bounded distance of the query) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric. Prior work proposed to build proximity search from inner product functional encryption (Kim et al., SCN 2018). This work identifies and closes two gaps in this approach: 1. Biometrics use long vectors, often with thousands of bits. Many inner product encryption schemes have to invert a matrix whose dimension scales with this size. Setup is then not feasible on commodity hardware. We introduce a technique that improves setup efficiency without harming accuracy. 2.Prior approaches leak distance between queries and all stored records. We propose a construction from function hiding, predicate, inner product encryption (Shen et al., TCC 2009) that avoids this leakage. Finally, we show that our scheme can be instantiated using symmetric pairing groups, which improves search efficiency. 

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2. Private Eyes: Zero-Leakage Iris Searchable Encryption

   Ha, Julie; Cachet, Chloe; Demarest, Luke; Ahmad, Sohaib; Fuller, Benjamin (June 2025, ACM)

   This work introduces Private Eyes, the first zero-leakage biometric database. The only leakage of the system is unavoidable: 1) the log of the dataset size and 2) the fact that a query occurred. Private Eyes is built from oblivious symmetric searchable encryption. Approximate proximity queries are used: given a noisy reading of a biometric, the goal is to retrieve all stored records that are close enough according to a distance metric. Private Eyes combines locality sensitive-hashing or LSHs (Indyk and Motwani, STOC 1998) and oblivious maps which map keywords to values. One computes many LSHs of each record in the database and uses these hashes as keywords in the oblivious map with the matching biometric readings concatenated as the value. At search time with a noisy reading, one computes the LSHs and retrieves the disjunction of the resulting values from the map. The underlying oblivious map needs to answer disjunction queries efficiently. We focus on the iris biometric which requires a large number of LSHs, approximately 1000. Boldyreva and Tang’s (PoPETS 2021) design yields a suitable map for a small number of LSHs (their application was in zeroleakage k-nearest-neighbor search). Our solution is a zero-leakage disjunctive map designed for the setting when most clauses do not match any records. For the iris, on average at most 6% of LSHs match any stored value. We evaluate using the ND-0405 dataset; this dataset has 356 irises suitable for testing. To scale our evaluation, we use a generative adversarial network to produce synthetic irises. Accurate statistics on sizes beyond available datasets is crucial to optimizing the cryptographic primitives. This tool may be of independent interest. For the largest tested parameters of a 5000 synthetic iris database, a search requires 18 rounds of communication and 25ms of parallel computation. Our scheme is implemented and open-sourced. 

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3. Iris Print Attack Detection using Eye Movement Signals

   https://doi.org/10.1145/3517031.3532521  

   Raju, Mehedi Hasan; Lohr, Dillon J; Komogortsev, Oleg (January 2022, 2022 Symposium on Eye Tracking Research and Applications)

   Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art. 

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4. Fragile Bits in Off-angle Iris Recognition

   https://doi.org/10.1109/IWBF50991.2021.9465098  

   Karakaya, Mahmut (May 2021, 2021 IEEE International Workshop on Biometrics and Forensics)

   null (Ed.)

   As an emerging biometric research, standoff iris recognition systems focus on recognition of non-cooperative subjects in much less constrained environments where their captured images are likely to be non-ideal including being off-angle. Iris biometrics convert unwrapped iris textures into binary iris codes to compare them with other saved codes by measuring their Hamming Distances. The similarity calculation assumes an equal contribution of each individual pixel in iris codes. However, previous studies showed that some pixels (aka. fragile bits) are more error prone than others even in frontal iris images. In addition, off-angle iris images are affected by several challenging factors including corneal refraction and limbus occlusion. These challenges in off-angle images also increase the fragility of bits in iris codes. This paper first presents the pixel inconsistency in iris codes of off-angle images using elliptical segmentation and normalization. The pixel fragility is a result of iris codes warping due to the refraction of light in cornea and occlusion of iris texture at limbus. As another contribution, we propose to identify these fragile pixels in iris codes using edge detection and eliminating them in Hamming distance calculation by masking these fragile bits. Based on the results, the proposed method improves the recognition performance in off-angle iris images where the average genuine Hamming distance score reduced from 0.3082 to 0.1244 and the equal error rate is lowered 19%. 

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5. ARKGraph: All-Range Approximate K-Nearest-Neighbor Graph

   https://doi.org/10.14778/3603581.3603601  

   Zuo, Chaoji; Deng, Dong (June 2023, Proceedings of the VLDB Endowment)

   Given a collection of vectors, the approximate K-nearest-neighbor graph (KGraph for short) connects every vector to its approximate K-nearest-neighbors (KNN for short). KGraph plays an important role in high dimensional data visualization, semantic search, manifold learning, and machine learning. The vectors are typically vector representations of real-world objects (e.g., images and documents), which often come with a few structured attributes, such as times-tamps and locations. In this paper, we study the all-range approximate K-nearest-neighbor graph (ARKGraph) problem. Specifically, given a collection of vectors, each associated with a numerical search key (e.g., a timestamp), we aim to build an index that takes a search key range as the query and returns the KGraph of vectors whose search keys are within the query range. ARKGraph can facilitate interactive high dimensional data visualization, data mining, etc. A key challenge of this problem is the huge index size. This is because, given n vectors, a brute-force index stores a KGraph for every search key range, which results in O (K n 3 ) index size as there are O ( n 2 ) search key ranges and each KGraph takes O (K n ) space. We observe that the KNN of a vector in nearby ranges are often the same, which can be grouped together to save space. Based on this observation, we propose a series of novel techniques that reduce the index size significantly to just O (K n log n ) in the average case. Furthermore, we develop an efficient indexing algorithm that constructs the optimized ARKGraph index directly without exhaustively calculating the distance between every pair of vectors. To process a query, for each vector in the query range, we only need O (log log n + K log K) to restore its KNN in the query range from the optimized ARKGraph index. We conducted extensive experiments on real-world datasets. Experimental results show that our optimized ARKGraph index achieved a small index size, low query latency, and good scalability. Specifically, our approach was 1000x faster than the baseline method that builds a KGraph for all the vectors in the query range on-the-fly. 

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