EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Securit

Transparent authentication methodology in electronic education

In the context of on-line assessment in e-learning, a problem arises when a student taking an exam may wish to cheat by handing over personal credentials to someone else to take their place in an exam, Another problem is that there is no method for signing digital content as it is being produced in a computerized environment. Our proposed solution is to digitally sign the participant’s work by embedding voice samples in the transcript paper at regular intervals. In this investigation, we have demonstrated that a transparent stenographic methodology will provide an innovative and practical solution for achieving continuous authentication in an online educational environment by successful insertion and extraction of audio digital signatures

Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.Comment: arXiv admin note: text overlap with arXiv:2004.0362

An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics

Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201

Intensifying the Security of Multiomodal Biometric Authentication System using Watermarking

In Multimodal biometrics system two or more biometric attributes are combined which makes it far more secure than unimodal system as it nullifies all the vulnerabilities of it. But with the prompt ontogenesis of information technology, even the biometric data is not secure. There is one such technique that is implemented to secure the biometric data from inadvertent or deliberate attacks is known as Digital watermarking. This paper postulate an approach that is devise in both the directions of enlarging the security through watermarking technique and improving the efficiency of biometric identification system by going multimodal. Three biometric traits are consider in this paper two of them are physical traits i.e. ; face, fingerprint and one is behavioral trait (signature).The biometric traits are initially metamorphose using Discrete Wavelet and Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction and presented results rationalize the effectiveness of the scheme

Application of Stochastic Diffusion for Hiding High Fidelity Encrypted Images

Cryptography coupled with information hiding has received increased attention in recent years and has become a major research theme because of the importance of protecting encrypted information in any Electronic Data Interchange system in a way that is both discrete and covert. One of the essential limitations in any cryptography system is that the encrypted data provides an indication on its importance which arouses suspicion and makes it vulnerable to attack. Information hiding of Steganography provides a potential solution to this issue by making the data imperceptible, the security of the hidden information being a threat only if its existence is detected through Steganalysis. This paper focuses on a study methods for hiding encrypted information, specifically, methods that encrypt data before embedding in host data where the ‘data’ is in the form of a full colour digital image. Such methods provide a greater level of data security especially when the information is to be submitted over the Internet, for example, since a potential attacker needs to first detect, then extract and then decrypt the embedded data in order to recover the original information. After providing an extensive survey of the current methods available, we present a new method of encrypting and then hiding full colour images in three full colour host images with out loss of fidelity following data extraction and decryption. The application of this technique, which is based on a technique called ‘Stochastic Diffusion’ are wide ranging and include covert image information interchange, digital image authentication, video authentication, copyright protection and digital rights management of image data in general

Information Forensics and Security: A quarter-century-long journey

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends

Biometric Privacy Protection based on Combination of Hiding and Chaotic Encryption

 نتيجة لاستخدام أنظمة القياسات الحيوية بكثرة، أصبحت سلامة ميزة القياسات الحيوية ذات أهمية كبيرة. عندما يتم نقل الصور البيومترية عبر قنوات غير آمنة أو تخزينها كبيانات أولية ، فإنها تصبح عرضة لخطر السرقة والتزوير والهجوم. يعد إخفاء البيانات أحد الأساليب الرئيسية لحماية الخصوصية. الهدف من إخفاء البيانات البيومترية هو تضمين البيانات الشخصية ززفي غلاف القياسات الحيوية والحفاظ على أداء التعرف. تقدم الفكرة الورقية مستويين من الأمان يعتمدان على الإخفاء والتشفير. يتم تجزأت صورة العين إلى جزئيين او منطقتين هما  (ROI)  (NROI) وبقصد بهما منطقة مهمة ومنطقة غير مهمة .يتم تضمين بيانات الخصوصية مع NROI ثم إعادة تجميع الصورة باستخدام ROI  للقزحية للحصول على صورة مدمجة. ثم يتم تطبيق التشفير العشوائي على الصورة المضمنة للحصول على مستوى عالٍ من الأمان. تم اختبار النتائج التجريبية باستخدام مجموعة بيانات CASIA1.تم استخدام مقياسيين هما PSNR و NC. أظهرت نتائج الاختبار قيمة عالية لل  PSNR مما يعني احتفاظ صورة الغلاف بجودتها وقيمة NC هي (1) مما يعني استرجاع مثالي للبيانات السرية. كما وتم اختبار طريقة التشفير باستخدام قياسات مثل الرسم البياني والارتباط والنتروبيا وجميع النتائج كانت جيدة.With the expanded use of biometric systems, the safety of the biometric feature has become increasingly important. When biometric images are transferred through unsafe channels or stored as raw data, they become at risk of theft, forgery and attack. Data hiding is one of the main techniques of Privacy Protection. The goal of biometric data hiding is for adequate personal data is to be included in the cover of Biometrics and to maintain recognition performance. The paper idea introduces two levels of security based on hiding and encryption. The eye image is segmented into two regions Region of Interest (ROI) and Non-Region of Interest (NROI), The iris segmentation method depends on the Circular Hough Transform (CHT). The privacy data is embedded with NROI and then reassemble the image with ROI (iris) to get the embedding image. Then chaotic encryption is applied on the embedded image to get a high level of security. The experimental results are tested using the CASIA1 data set. The tests of hiding level are done using measurements such as PSNR and NC. The results show that the suggested method gives a higher value of PSNR which means not destroy the cover image and the value of NC is (1) which means a perfect reconstruction of secret data. The tests on encryption levels show good results using measurements such as histogram, correlation, and entropy