Bits Of Entropy Research Articles

Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

Content-Based Image Retrieval (CBIR) uses complicated algorithms to analyze visual attributes and retrieve relevant photos from large databases. CBIR is essential to a privacy-preserving feature extraction and protection method for outsourced picture representation. SecureImageSec combines essential methods with the system’s key entities to ensure secure, private and protected image feature processing during outsourcing. For a system to be implemented effectively, these techniques must be seamlessly integrated across critical entities, such as the client, the cloud server that is being outsourced, the component that protects secure features, the component that maintains privacy in communication, access control, and authorization, and the integration and system evaluation. The client entity initiates outsourcing using advanced encryption techniques to protect privacy. SecureImageSec protects outsourced data by using cutting-edge technologies like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). Cloud servers hold secure feature protection entities and protect outsourced features’ privacy and security. SecureImageSec uses AES and FPE to protect data format. SecureImageSec’s cloud-outsourced privacy-preserving communication uses SSL/TLS and QKD to protect data transmission. Attribute-Based Encryption (ABE) and Functional Encryption (FE) in SecureImageSec limit access to outsourced features based on user attributes and allow fine-grained access control over decrypted data. SecureImageSec’s Information Leakage Rate (ILR) of 0.02 for a 1000-feature dataset shows its efficacy. SecureImageSec also achieves 4.5 bits of entropy, ensuring the encrypted feature set’s muscular cryptographic strength and randomness. Finally, SecureImageSec provides secure and private feature extraction and protection, including CBIR capabilities, for picture representation outsourcing.

GazePair: Efficient Pairing of Augmented Reality Devices Using Gaze Tracking

As Augmented Reality (AR) devices become more prevalent and commercially viable, the need for quick, efficient, and secure schemes for pairing these devices has become more pressing. Current methods to securely exchange holograms require users to send this information through large data centers, creating security and privacy concerns. Existing techniques to pair these devices on a local network and share information fall short in terms of usability and scalability. These techniques either require hardware not available on AR devices, intricate physical gestures, removal of the device from the head, do not scale to multiple pairing partners, or rely on methods with low entropy to create encryption keys. To that end, we propose a novel pairing system, called GazePair, that improves on all existing local pairing techniques by creating an efficient, effective, and intuitive pairing protocol. GazePair uses eye gaze tracking and a spoken key sequence cue (KSC) to generate identical, independently generated symmetric encryption keys with 64 bits of entropy. GazePair also achieves improvements in pairing success rates and times over current methods. Additionally, we show that GazePair can extend to multiple users. Finally, we assert that GazePair can be used on any Mixed Reality (MR) device equipped with eye gaze tracking.

An Evaluation On The Entropy Supplying Capability Of Smartphone Sensors

Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.

Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks

Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample&#x2019;s identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30&#x0025; of the population, for any target from that population, the uncertainty of the target&#x2019;s identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> to facilitate privacy-preserving data sharing and computation in genome-wide analysis. <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> mitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> outperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.

A Captcha-Based Graphical Password with Strong Password Space and Usability Study

stract: Security for authentication is required to give a superlative secure users’ personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued- recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall- based n×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space (2.4 × 104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone

Quantum encryption of superposition states with quantum permutation pad in IBM quantum computers

We present an implementation of Kuang and Bettenburg’s Quantum Permutation Pad (QPP) used to encrypt superposition states. The project was conducted on currently available IBM quantum systems using the Qiskit development kit. This work extends previously reported implementation of QPP used to encrypt basis states and demonstrates that application of the QPP scheme is not limited to the encryption of basis states. For this implementation, a pad of 56 2-qubit Permutation matrices was used, providing 256 bits of entropy for the QPP algorithm. An image of a cat was used as the plaintext for this experiment. The plaintext was randomized using a classical XOR function prior to the state preparation procedure. To create corresponding superposition states, we applied a novel operator defined in this paper. These superposition states were then encrypted using QPP, with 2-qubit Permutation Operators, producing superposition ciphertext states. Due to the lack of a quantum channel, we omitted the transmission and executed the decryption procedure on the same IBM quantum system. If a quantum channel existed, the superposition ciphertext states could be transmitted as qubits, and be directly decrypted on a different quantum system. We provide a brief discussion of the security, although the focus of the paper remains on the implementation. Previously we have demonstrated QPP operating in both classical and quantum computers, offering an interesting opportunity to bridge the security gap between classical and quantum systems. This work broadens the applicability of QPP for the encryption of basis states as well as superposition states. We believe that quantum encryption schemes that are not limited to basis states will be integral to a secure quantum internet, to reduce vulnerabilities introduced by using two separate algorithms for secure communication between a quantum and a classical computer.

Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances

Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive information about participants that could be utilized for unethical activities, such as blackmailing, identity theft, mass surveillance, or social engineering. Data anonymization is a standard step during data collection, before sharing, to overcome the risk of disclosure. However, conventional data anonymization techniques are not foolproof and also hinder the opportunity for personalized evaluations. Much research has been done for synthetic data generation using generative adversarial networks and many other machine learning methods; however, these methods are either not free to use or are limited in capacity. This study evaluates the performance of an emerging tool named synthpop, an R package producing synthetic data as an alternative approach for data anonymization. This paper establishes data standards derived from the original data set based on the utilities and quality of information and measures variations in the synthetic data set to evaluate the performance of the data synthesis process. The methods to assess the utility of the synthetic data set can be broadly divided into two approaches: general utility and specific utility. General utility assesses whether synthetic data have overall similarities in the statistical properties and multivariate relationships with the original data set. Simultaneously, the specific utility assesses the similarity of a fitted model’s performance on the synthetic data to its performance on the original data. The quality of information is assessed by comparing variations in entropy bits and mutual information to response variables within the original and synthetic data sets. The study reveals that synthetic data succeeded at all utility tests with a statistically non-significant difference and not only preserved the utilities but also preserved the complexity of the original data set according to the data standard established in this study. Therefore, synthpop fulfills all the necessities and unfolds a wide range of opportunities for the research community, including easy data sharing and information protection.

Quantum encryption with quantum permutation pad in IBMQ systems

Quantum permutation pad or QPP is a quantum-safe symmetric cryptographic algorithm proposed by Kuang and Bettenburg in 2020. The theoretical foundation of QPP leverages the linear algebraic representations of quantum gates which makes QPP realizable in both, quantum and classical systems. By applying the QPP with 64 of 8-bit permutation gates, holding respective entropy of over 100,000 bits, we accomplished quantum random number distributions digitally over today’s classical internet. The QPP has also been used to create pseudo quantum random numbers and served as a foundation for quantum-safe lightweight block and streaming ciphers. This paper continues to explore numerous applications of QPP, namely, we present an implementation of QPP as a quantum encryption circuit on today’s still noisy quantum computers. With the publicly available 5-qubit IBMQ devices, we demonstrate quantum secure encryption (256 bits of entropy) using 2-qubit QPP with 56 permutation gates, and 3-qubit QPP with 17 permutation gates respectively. Initial qubits of the encryption circuit correspond to the plaintext and after applying quantum encryption operations, cipher qubits are measured with probabilistic distributions, and the results with the highest probability are recorded as cipher bits. The cipher bits are then decrypted with an inverse QPP circuit. The output state plaintext qubits are measured and the most frequent count measurement results are recorded as plaintext bits. This quantum encryption and decryption process clearly demonstrates that QPP quantum implementations works exactly as symmetric encryption and decryption schemes should. The plaintext and ciphertext bits can also be encrypted and decrypted respectively by any classical computing device with the corresponding QPP algorithm as in quantum computers. This work reveals that it is possible to build quantum-secure communications between quantum-to-quantum and quantum-to-classical computers over today’s internet and the future quantum internet.

Reversible inter-degree-of-freedom optical-coherence conversion via entropy swapping.

The entropy associated with an optical field quantifies the field fluctuations and thus its coherence. Any binary optical degree-of-freedom (DoF) - such as polarization or the field at a pair of points in space - can each carry up to one bit of entropy. We demonstrate here that entropy can be reversibly swapped between different DoFs, such that coherence is converted back and forth between them without loss of energy. Specifically, starting with a spatially coherent but unpolarized field carrying one bit of entropy, we unitarily convert the coherence from the spatial DoF to polarization to produce a spatially incoherent but polarized field by swapping the entropy between the two DoFs. Next, we implement the inverse unitary operator, thus converting the coherence back to yield once again a spatially coherent yet unpolarized field. We exploit the intermediate stage between the two coherence conversions - where the spatial coherence has been converted to the polarization DoF - to verify that the field has become immune to the deleterious impact of spatial phase scrambling. Maximizing the spatial entropy protects the spatial DoF by preventing it from taking on any additional fluctuations. After the second coherence conversion, spatial coherence is readily retrieved, and the effect of spatial phase scrambling circumvented.

Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles

The development of CRISPR-based barcoding methods creates an exciting opportunity to understand cellular phylogenies. We present a compact, tunable, high-capacity Cas12a barcoding system called dual acting inverted site array (DAISY). We combined high-throughput screening and machine learning to predict and optimize the 60-bp DAISY barcode sequences. After optimization, top-performing barcodes had ∼10-fold increased capacity relative to the best random-screened designs and performed reliably across diverse cell types. DAISY barcode arrays generated ∼12 bits of entropy and ∼66,000 unique barcodes. Thus, DAISY barcodes-at a fraction of the size of Cas9 barcodes-achieved high-capacity barcoding. We coupled DAISY barcoding with single-cell RNA-seq to recover lineages and gene expression profiles from ∼47,000 human melanoma cells. A single DAISY barcode recovered up to ∼700 lineages from one parental cell. This analysis revealed heritable single-cell gene expression and potential epigenetic modulation of memory gene transcription. Overall, Cas12a DAISY barcoding is an efficient tool for investigating cell-state dynamics.

The Wiretap Channel for Capacitive PUF-Based Security Enclosures

In order to protect devices from physical manipulations, protective security enclosures were developed. However, these battery-backed solutions come with a reduced lifetime, and have to be actively and continuously monitored.In order to overcome these drawbacks, batteryless capacitive enclosures based on Physical Unclonable Functions (PUFs) have been developed that generate a keyencryption-key (KEK) for decryption of the key chain. In order to reproduce the PUF-key reliably and to compensate the effect of noise and environmental influences, the key generation includes error correction codes. However, drilling attacks that aim at partially destroying the enclosure also alter the PUF-response and are subjected to the same error correction procedures. Correcting attack effects, however, is highly undesirable as it would destroy the security concept of the enclosure. In general, designing error correction codes such that they provide tamper-sensitivity to attacks, while still correcting noise and environmental effects is a challenging task.We tackle this problem by first analyzing the behavior of the PUF-response under external influences and different post-processing parameters. From this, we derive a system model of the PUF-based enclosure, and construct a wiretap channel implementation from q-ary polar codes. We verify the obtained error correction scheme in a Monte Carlo simulation and demonstrate that our wiretap channel implementation achieves a physical layer security of 100 bits for 306 bits of entropy for the PUF-secret. Through this, we further develop capacitive PUF-based security enclosures and bring them one step closer to their commercial deployment.

Quantum Encryption and Decryption in IBMQ Systems using Quantum Permutation Pad

We present a functioning implementation of Kuang et al.’s Quantum Permutation Pad (QPP) using the Qiskit developmental kit on the currently available International Business Machines (IBM) quantum computers. For this implementation, we use a pad with 28 2-qubit permutation gates that provides 128 bits of entropy. In this implementation, we divide the plaintext into blocks of 2-bits each. Each such block is encrypted one at a time. For any given block of plaintext, a quantum circuit is created with qubits initialized according to the given plaintext 2-bit block. The plaintext qubits are then acted on with 2-qubit permutation operators chosen from a 28- permutation QPP pad. Due to the inability to send qubits directly, the ciphertext qubits are measured and transmitted to the decrypting side over a classical channel. The decryption can be performed on either a classical or quantum computer. The decryption uses an inverse Quantum Permutation Pad with the Hermitian conjugates of the corresponding permutation gates used for the encryption. We are currently working on advancing the implementation of QPP to include additional steps for security and efficiency.

Coded Modulation and Shaping for Multivalued Physical Unclonable Functions

In this paper, a coded modulation scheme employing signal shaping is proposed and designed for use in multi-valued physical unclonable functions (PUFs). In the literature, usually binary PUFs are considered, where less than one bit of entropy is extracted from each fundamental building block (PUF node). This randomness in the hardware may then used for the derivation of cryptographic keys. Meanwhile, PUFs over higher-order alphabets, so-called multi-valued PUFs (MVPUFs) have been presented. Typically, symbols from a higher-order alphabet are extracted by suited quantization. In contrast, in this paper we propose a coded modulation scheme which directly works on the analog values provided by the PUF nodes. We establish an interpretation as digital transmission with randomness at the transmitter, and thereon calculate the respective capacities of bit levels for the Gaussian distributed reference PUF readout in additive Gaussian noise. In addition, the metric for optimum soft-decision decoding is derived. Thereby, the coded modulation scheme can be designed for the present situation. A helper data scheme adapted to the given situation is proposed and analyzed. Moreover, on top of the multilevel coding scheme, we employ signal shaping for the first time in the context of PUFs. In particular, trellis shaping is applied. By this, the coded modulation scheme can be perfectly matched to the PUF statistics. This is reflected by the results of conducted numerical simulations. High coding rates can be used, i.e., a large entropy per PUF node can be extracted, with very high reliabilities.

Measuring the Thermodynamic Cost of Timekeeping

All clocks, in some form or another, use the evolution of nature towards higher entropy states to quantify the passage of time. Due to the statistical nature of the second law and corresponding entropy flows, fluctuations fundamentally limit the performance of any clock. This suggests a deep relation between the increase in entropy and the quality of clock ticks. Indeed, minimal models for autonomous clocks in the quantum realm revealed that a linear relation can be derived, where for a limited regime every bit of entropy linearly increases the accuracy of quantum clocks. But can such a linear relation persist as we move towards a more classical system? We answer this in the affirmative by presenting the first experimental investigation of this thermodynamic relation in a nanoscale clock. We stochastically drive a nanometer-thick membrane and read out its displacement with a radio-frequency cavity, allowing us to identify the ticks of a clock. We show theoretically that the maximum possible accuracy for this classical clock is proportional to the entropy created per tick, similar to the known limit for a weakly coupled quantum clock but with a different proportionality constant. We measure both the accuracy and the entropy. Once non-thermal noise is accounted for, we find that there is a linear relation between accuracy and entropy and that the clock operates within an order of magnitude of the theoretical bound.

Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting

Polymer banknotes are the trend for printed currency and have been adopted by more than fifty countries worldwide. However, over the past years, the quantity of polymer counterfeits has been increasing, so has the quality of counterfeits. This shows that the initial advantage of bringing a new polymer technology to fight against counterfeiting is reducing. To maintain one step ahead of counterfeiters, we propose a novel anti-counterfeiting technique called Polymer Substrate Fingerprinting (PSF). Our technique is built based on the observation that the opacity coating, a critical step during the production of polymer notes, is a stochastic manufacturing process, leaving uneven thickness in the coating layer and the random dispersion of impurities from the ink. The imperfections in the coating layer result in random translucent patterns when a polymer banknote is back-lit by a light source. We show these patterns can be reliably captured by a commodity negative-film scanner and processed into a compact fingerprint to uniquely identify each banknote. Using an extensive dataset of 6,200 sample images collected from 340 UK banknotes, we show that our method can reliably authenticate banknotes, and is robust against rough daily handling of banknotes. Furthermore, we show the extracted fingerprints contain around 900 bits of entropy, which makes it extremely scalable to identify every polymer note circulated globally. As compared with previous or existing anti-counterfeiting mechanisms for banknotes, our method has a distinctive advantage: it ensures that even in the extreme case when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible because of the difficulty to replicate a stochastic manufacturing process.

Factory Calibration Fingerprinting of Sensors

Device fingerprinting aims to generate a distinctive signature, or fingerprint, that uniquely identifies individual computing devices. Fingerprints may be a privacy concern since apps and websites can use them to track user activity online. To protect user privacy, both Android and iOS have included a variety of measures to prevent such tracking. In this paper we present a new type of fingerprinting, factory calibration fingerprinting, that bypasses existing tracking protection. Our attack recovers embedded per-device factory calibration data from the accelerometer, gyroscope, and magnetometer sensors that are pervasive in modern smartphones by careful analysis of the sensor output alone. We discuss the factory calibration behaviour of each sensor and show that the calibration fingerprint is fast to generate, does not change over time or after a factory reset, and can be used to track users across apps and websites without any special permission from the user. We find the calibration fingerprint is very likely to be globally unique for iOS devices, with an estimated 67 bits of entropy for the iPhone 6S. In addition, we have analysed 146 Android device models from 11 vendors and found the attack also works on recent Google Pixel devices. For Pixel 4/4 XL, we estimate the calibration fingerprint provides about 57 bits of entropy. Following our disclosures, Apple deployed a mitigation in iOS 12.2 and Google in Android 11. We analyse Apple's fix and show that the mitigation is imperfect although it is likely to be sufficient in most threat models.

Restorative potential, fascination, and extent for designed digital landscape models

Few studies have investigated whether manipulating objective measures related to fascination and extent affect respondents’ ratings of restorative potential (RP) and estimations of fascination and extent. The following study addresses this need. We manipulated or measured variables in 27 color digital landscape model views. Tree height represented a measure of fascination. Three measures related to extent: The number of organized plant groups represented coherence. Shannon’s Information Entropy bit values represented plant species complexity. The visible view polygon area in each model represented scope. We included 65 respondents’ RP ratings for the digital model views in analyses, along with estimations of fascination (n=48), extent (n=43), coherence (n=38), complexity (n=44), and scope (n=35). Collinearity diagnostics indicated dependency between respondents’ estimations of fascination, extent, and complexity, and between estimations of coherence and scope. A strong, inverse correlation occurred between respondents’ RP ratings and the view polygon area. Repeated measures ANOVA test results suggest that respondents’ RP ratings increased as mean designed tree height increased. RP ratings for model views depicting scattered and formally arranged plants were significantly higher than views of clustered plants. Moreover, the decrease in RP ratings between scattered and clustered scenes was greater when plants represented three bits of entropy instead of two. Chief among the implications stemming from this study is that increasingly taller trees and groundcover plants may have increasingly greater restorative potential.

New Graphic Password Scheme Containing Questions-Background-Pattern and Implementation

Security of authentication is needed to be provided superlatively to secure users’ personal and exchange information since online information exchange systems have been developed according to internet speed. Therefore, the aim of this paper is to develop existing graphical password scheme based on one's memory, create and implement a new graphical password scheme that composed of three layer verification. We programmed our scheme in order to prove its superiority comparing with existing schemes. While we conducted survey on user by accessing participant to our system lied in participants’ local network and we analyzed in accordance with the average length of their created password and statistical significant of entropy bit. From the survey results of total participants, we find that our scheme has statistical significant, furthermore it was proved that it can secure from a variety of attacks due to its high entropy.

Evaluations of landscape preference, complexity, and coherence for designed digital landscape models

Few studies have investigated whether intentionally manipulating objective measures related to the informational concepts of complexity and coherence, within the context of natural and built settings, affects respondents’ preference ratings and estimations of complexity and coherence. Our study addresses this research need. Color digital landscape model views depicted three variations in the number of unique plant species (complexity), translated into Shannon’s information entropy bit values, and three methods of plant organization (coherence)—formal, clustered, and scattershot—within field, residential, and urban settings. Participants viewed and evaluated the digital landscape model views for preference (n=77) or estimated the presence of complexity (n=34) or coherence (n=38). Strong, direct correlations resulted between respondents’ estimations of complexity and designed entropy bit values. Respondents’ estimations of coherence inversely correlated with the number of regions depicted in model views that represent the methods of plant organization. Preference did not correlate with estimations of coherence or designed entropy values, but did correlate with estimations of complexity. Repeated measures ANOVA test results suggest that respondents’ preference for scenes increased as entropy values increased between two and four bits, and that plants arranged in clusters were liked more than scattershot or formal compositions. Moreover, respondents liked residential settings significantly more than urban settings, and preference increased significantly more with increasing entropy values in urban settings than in residential. The interaction between setting and entropy values for depictions of clustered and formal plant arrangements affected preference differently in each setting.

Information Theory and the IrisCode

Iris recognition has legendary resistance to false matches, and the tools of information theory can help to explain why. The concept of entropy is fundamental to understanding biometric collision avoidance. This paper analyses the bit sequences of IrisCodes computed both from real iris images and from synthetic white noise iris images, whose pixel values are random and uncorrelated. The capacity of the IrisCode as a channel is found to be 0.566 bits per bit encoded, of which 0.469 bits of entropy per bit is encoded from natural iris images. The difference between these two rates reflects the existence of anatomical correlations within a natural iris, and the remaining gap from one full bit of entropy per bit encoded reflects the correlations in both phase and amplitude introduced by the Gabor wavelets underlying the IrisCode. A simple two-state hidden Markov model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their imposter distributions, and may be useful for generating large synthetic IrisCode databases.