اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدData hiding in Fingerprint Minutiae Template for Privacy Protection
131
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper, we propose a novel scheme for data hiding in the fingerprint minutiae template, which is the most popular in fingerprint recognition systems. Various strategies are proposed in data embedding in order to maintain the accuracy of fingerprint recognition as well as the undetectability of data hiding. In bits replacement based data embedding, we replace the last few bits of each element of the original minutiae template with the data to be hidden. This strategy can be further improved using an optimized bits replacement based data embedding, which is able to minimize the impact of data hiding on the performance of fingerprint recognition. The third strategy is an order preserving mechanism which is proposed to reduce the detectability of data hiding. By using such a mechanism, it would be difficult for the attacker to differentiate the minutiae template with hidden data from the original minutiae templates. The experimental results show that the proposed data hiding scheme achieves sufficient capacity for hiding common personal data, where the accuracy of fingerprint recognition is acceptable after the data hiding.
قيم البحث
اقرأ أيضاً
Data hiding is referred to as the art of hiding secret data into a digital cover for covert communication. In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep
neural network (DNN) with an ordinary task. After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal. In other words, the tools of data hiding are hidden to avoid arousing suspicion.
With the rapid increase in software exploits, the last few decades have seen several hardware-level features to enhance security (e.g., Intel MPX, ARM TrustZone, Intel SGX, Intel CET). Due to security, performance and/or usability issues these featur
es have attracted steady criticism. One such feature is the Intel Memory Protection Extensions (MPX), an instruction set architecture extension promising spatial memory safety at a lower performance cost due to hardware-accelerated bounds checking. However, recent investigations into MPX have found that is neither as performant, accurate, nor precise as cutting-edge software-based spatial memory safety. As a direct consequence, compiler and operating system support for MPX is dying, and Intel has begun to manufacture desktop CPUs without MPX. Nonetheless, given how ubiquitous MPX is, it provides an excellent yet under-utilized hardware resource that can be aptly salvaged for security purposes. In this paper, we propose Simplex, a library framework that re-purposes MPX registers as general purpose registers. Using Simplex, we demonstrate how MPX registers can be used to store sensitive information (e.g., encryption keys) directly on the hardware. We evaluate Simplex for performance and find that its overhead is small enough to permit its deployment in all but the most performance-intensive code. We refactored the string.h buffer manipulation functions and found a geometric mean 0.9% performance overhead. We also modified the deepsjeng and lbm SPEC CPU2017 benchmarks to use Simplex and found a 1% and 0.98% performance overhead respectively. Finally, we investigate the behavior of the MPX context with regards to multi-process and multi-thread programs.
Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new wa
y to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: https://github.com/felixTY/FingerNet.
Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relat
ion provides a great amount of information and can be used in other scenarios. In this paper, we propose the mapping distortion based protection (MDP) and its augmentation-based extension (AugMDP) to protect the data privacy by modifying the original dataset. In the modified dataset generated by MDP, the image and its label are not consistent ($e.g.$, a cat-like image is labeled as the dog), whereas the DNNs trained on it can still achieve good performance on benign testing set. As such, this method can protect privacy when the dataset is leaked. Extensive experiments are conducted, which verify the effectiveness and feasibility of our method. The code for reproducing main results is available at url{https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion}.
The proportion of Energy consumption in the building industry is great, as well as the amount of cooling and heating system. Scholars have been working on energy conservation of Heating, ventilation, and air-conditioning and other systems in building
s. The application of occupant behavior data for building energy optimization has started gaining attention from scholars. However, occupant behavior data concerns many aspects of occupants privacy. Different types of occupant behavior data contain occupants private information to different levels. It is crucial to conduct privacy protection of occupant behavior data when using occupant behavior for energy conservation. This paper presents the aspects of privacy issue when using occupant behavior data, and methods to protect data privacy with blockchain technology. Both two options of using blockchain for privacy protection, sending data records as transactions and storing files on the blockchain, are explained and evaluated with temperature records from an open access paper. Sending data as transactions can be used between sensors and local building management system. While storing files on blockchain can be used for collaboration of different building management systems. Advantages, drawbacks, and potentials of using blockchain for data and file transfer are discussed. The results should be helpful for using occupant behavior data for building energy optimization.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
