Product Subset Problem : Applications to number theory and cryptography

We consider applications of Subset Product Problem (SPP) in number theory and cryptography. We obtain a probabilistic algorithm that attack SPP and we analyze it with respect time/space complexity and success probability. In fact we provide an application to the problem of finding Carmichael numbers and an attack to Naccache-Stern knapsack cryptosystem, where we update previous results.Comment: 17 pages, 2 figures, LaTeX; references added, typos corrected, a new figure was inserted, sections 2.1, 2.2 improve

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

On all-or-nothing transforms and password-authenticated key exchange protocols

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 142-152).by Victor Boyko.Ph.D

Privacy Enhancing Protocols using Pairing Based Cryptography

This thesis presents privacy enhanced cryptographic constructions, consisting of formal definitions, algorithms and motivating applications. The contributions are a step towards the development of cryptosystems which, from the design phase, incorporate privacy as a primary goal. Privacy offers a form of protection over personal and other sensitive data to individuals, and has been the subject of much study in recent years. Our constructions are based on a special type of algebraic group called bilinear groups. We present existing cryptographic constructions which use bilinear pairings, namely Identity-Based Encryption (IBE). We define a desirable property of digital signatures, blindness, and present new IBE constructions which incorporate this property. Blindness is a desirable feature from a privacy perspective as it allows an individual to obscure elements such as personal details in the data it presents to a third party. In IBE, blinding focuses on obscuring elements of the identity string which an individual presents to the key generation centre. This protects an individual's privacy in a direct manner by allowing her to blind sensitive elements of the identity string and also prevents a key generation centre from subsequently producing decryption keys using her full identity string. Using blinding techniques, the key generation centre does not learn the full identity string. In this thesis, we study selected provably-secure cryptographic constructions. Our contribution is to reconsider the design of such constructions with a view to incorporating privacy. We present the new, privacy-enhanced cryptographic protocols using these constructions as primitives. We refine useful existing security notions and present feasible security definitions and proofs for these constructions

Security and Privacy in RFID Systems

This PhD thesis is concerned with authentication protocols using portable lightweight devices such as RFID tags. these devices have lately gained a significant attention for the diversity of the applications that could benefit form their features, ranging from inventory systems and building access control, to medical devices. However, the emergence of this technology has raised concerns about the possible loss of privacy carrying such tags induce in allowing tracing persons or unveiling the contents of a hidden package. this fear led to the appearance of several organizations which goal is to stop the spread of RFID tags. We take a cryptographic viewpoint on the issue and study the extent of security and privacy that RFID-based solutions can offer. In the first part of this thesis, we concentrate on analyzing two original primitives that were proposed to ensure security for RFID tags. the first one, HB#, is a dedicated authentication protocol that exclusively uses very simple arithmetic operations: bitwise AND and XOR. HB# was proven to be secure against a certain class of man-in-the-middle attacks and conjectured secure against more general ones. We show that the latter conjecture does not hold by describing a practical attack that allows an attacker to recover the tag's secret key. Moreover, we show that to be immune against our attack, HB#'s secret key size has to be increased to be more than 15 000 bits. this is an unpractical value for the considered applications. We then turn to SQUASH, a message authentication code built around a public-key encryption scheme, namely Rabin's scheme. By mounting a practical key recovery attack on the earlier version of SQUASH, we show that the security of all versions of SQUASH is unrelated to the security of Rabin encryption function. The second part of the thesis is dedicated to the privacy aspects related to the RFID technology. We first emphasize the importance of establishing a framework that correctly captures the intuition that a privacy-preserving protocol does not leak any information about its participants. For that, we show how several protocols that were supported by simple arguments, in contrast to a formal analysis, fail to ensure privacy. Namely, we target ProbIP, MARP, Auth2, YA-TRAP, YA-TRAP+, O-TRAP, RIPP-FS, and the Lim-Kwon protocol. We also illustrate the shortcomings of other privacy models such as the LBdM model. The rest of the dissertation is then dedicated to our privacy model. Contrarily to most RFID privacy models that limit privacy protection to the inability of linking the identity of two participants in two different protocol instances, we introduce a privacy model for RFID tags that proves to be the exact formalization of the intuition that a private protocol should not leak any information to the adversary. the model we introduce is a refinement of Vaudenay's one that invalidates a number of its limitations. Within these settings, we are able to show that the strongest notion of privacy, namely privacy against adversaries that have a prior knowledge of all the tags' secrets, is realizable. To instantiate an authentication protocol that achieves this level of privacy, we use plaintext-aware encryption schemes. We then extend our model to the case of mutual authentication where, in addition to a tag authenticating to the reader, the reverse operation is also required

Novel reversible text data de-identification techniques based on native data structures

Technological development in today's digital world has resulted in the collection and storage of large amounts of personal data. These data enable both direct services and non-direct activities, known as secondary use. The secondary use of data can improve decision-making, service experiences, and healthcare systems. However, the widespread reuse of personal data raises significant privacy and policy issues, especially for health- related information; these data may contain sensitive data, leading to privacy breaches if compromised. Legal systems establish laws to protect the privacy of personal data disclosed for secondary use. A well-known example is the General Data Protection Regulation (GDPR), which outlines a specific set of rules for sharing and storing personal data to protect individual privacy. The GDPR explicitly points to data de-identification, especially pseudonymization, as one measure that can help meet the requirements for the processing of personal data. The literature on privacy preservation approaches has largely been developed in the field of data anonymization, where personal data are irreversibly removed or obfuscated and there is no means by which to recover an individual's identity if needed. By contrast, pseudonymization is a promising technique to protect privacy while enabling the recovery of de-identified data. Significantly, many existing approaches for pseudonymization were developed long before the GDPR requirements were established, and so they may fail to satisfy its provisions. Therefore, it is worthwhile to offer technical solutions to preserve privacy while supporting the legitimate use of data. This thesis proposes a novel de-identification system for unstructured textual data, known as ARTPHIL, that generates de-identified data in compliance with the GDPR requirement for strong pseudonymization. The system was evaluated using 2014 i2b2 testing data. The proposed system achieved a recall of 96.93% in terms of detecting and encrypting personal health information, as specified under guidelines provided by the Health Insurance Portability and Accountability Act (HIPAA). The system used a novel and lightweight cryptography algorithm E-ART to encrypt personal data cost-effectively and without compromising security. The main novelty of the E-ART algorithm is the use of the reflection property of a balanced binary tree data structure as substitution method instead of complex and multiple iterations. The performance and security of the proposed algorithm were compared to two symmetric encryption algorithms: The Advanced Encryption Standard and Data Encryption Standard. The security analysis showed comparable results, but the performance analysis indicated that E‐ART had the shortest ciphertext and running time with comparable memory usage, which indicates the feasibility of using ARTPHIL for delay-sensitive or data-intensive application