arXiv subjects

Gerard Memmi

Publications and source records attributed to Gerard Memmi.

At least 19 recordsLinked to original sources

arXiv2025

Nicknames for Group Signatures

Nicknames for Group Signatures (NGS) is a new signature scheme that extends Group Signatures (GS) with Signatures with Flexible Public Keys (SFPK). Via GS, each member of a group can sign messages on behalf of the group without revealing his identity, except to a designated auditor. Via SFPK, anyone can create new identities for a particular user, enabling anonymous transfers with only the intended recipient able to trace these new identities. To prevent the potential abuses that this anonymity brings, NGS integrates flexible public keys into the GS framework to support auditable transfers. In addition to introducing NGS, we describe its security model and provide a mathematical construction proved secure in the Random Oracle Model. As a practical NGS use case, we build NickHat, a blockchain-based token-exchange prototype system on top of Ethereum.

cs.CR↗

arXiv2025

Semiflows, Home Spaces, and Home States, Applications to the Analysis of Parameterized Petri Nets

After rapidly recalling basic notations relatively to semiflows and Petri nets, we define F, the set of semiflows over Z that we associate with a specific class of invariants. We then focus on F+, the set of semiflows with non-negative coordinates which are important to study the behavior of a Petri net. We recall known behavioral properties attached to semiflows in F+ that we associate with two sets of bounds regarding boundedness then liveness. We recall the notions of home states and home spaces for which we regrouped old and new properties. We introduce a new result on the decidability of liveness under the existence of a home state. The notions of minimality of semiflows and minimality of supports allow us to define generating sets that are particularly critical to develop an effective analysis of invariants and behavioral properties of Petri nets such as boundedness or even liveness. We also recall three known decomposition theorems considering N, Q+, and Q respectively where the decomposition over N is being improved with a necessary and sufficient condition. We use the notion the notion of generating sets to show that extremums linked to the set of bounds mentioned here above, are indeed computable by providing their values. Two related Petri net modeling arithmetic operations (one of which represents an Euclidean division) illustrate how results on semiflows and home spaces can be methodically used in efficiently analyzing the liveness of the parameterized model and underlining the efficiency brought by the combination of these results.

cs.FL↗

arXiv2025

NickPay, an Auditable, Privacy-Preserving, Nickname-Based Payment System

In this paper, we describe the motivation, design, security properties, and a prototype implementation of NickPay, a new privacy-preserving yet auditable payment system built on top of the Ethereum blockchain platform. NickPay offers a strong level of privacy to participants and prevents successive payment transfers from being linked to their actual owners. It is providing the transparency that blockchains ensure and at the same time, preserving the possibility for a trusted authority to access sensitive information, e.g., for audit purposes or compliance with financial regulations. NickPay builds upon the Nicknames for Group Signatures (NGS) scheme, a new signing system based on dynamic ``nicknames'' for signers that extends the schemes of group signatures and signatures with flexible public keys. NGS enables identified group members to expose their flexible public keys, thus allowing direct and natural applications such as auditable private payment systems, NickPay being a blockchain-based prototype of these.

cs.CR↗

arXiv2024

Home Spaces and Invariants to Analyze Parameterized Petri Nets

This article focuses on comparing the notions of home spaces and invariants, in Transition Systems and more particularly, in Petri Nets as well as a variety of derived Petri Nets. After recalling basic notions of Petri Nets and semiflows, we then discuss important characteristics of finite generating sets for F, the set of all semiflows with integer coordinates of a given Petri Net. Then, we particularly focus on F+ the set of semiflows with non-negative coordinates. Minimality of semiflows and minimality of supports are critical to develop effective analysis of invariants and behavioral properties of Petri Nets such as boundedness or even liveness. We recall known decomposition theorems considering N, Q+, or Q. The result over N is being improved into a necessary and sufficient condition. In addition, we present general new results about the topology and the behavioral properties of a Petri Net, illustrating the importance of considering semiflows with non-negative coordinates. Then, we regroup a number of results around the notion of home space and home state applied to transition systems. Home spaces and semiflows are used to efficiently support the analysis of behavioral properties. In this regard, we present a methodology to analyze a Petri Nets by successive refinement of home spaces directly deduced from semiflows and apply it to analyze a parameterized example drawn from the telecommunication industry underlining the efficiency brought by using minimal semiflows of minimal supports as well as the new results on the topology of the model. This methodology is better articulated than in previous papers, and brings us closer to an automated analysis.

cs.DM↗

arXiv2023

Invariants and Home Spaces in Transition Systems and Petri Nets

This lecture note focuses on comparing the notions of invariance and home spaces in Transition Systems and more particularly, in Petri Nets. We also describe how linear algebra relates to these basic notions in Computer Science, how it can be used for extracting invariant properties from a parallel system described by a Labeled Transition System in general and a Petri Net in particular. We endeavor to regroup a number of algebraic results dispersed throughout the Petri Nets literature with the addition of new results around the notions of semiflows and generating sets. \newline Several extensive examples are given to illustrate how the notion of invariants and home spaces can be methodically utilized through basic arithmetic and algebra to prove behavioral properties of a Petri Net. Some additional thoughts on invariants and home spaces will conclude this note.

cs.FL↗

arXiv2022

A Note on Generating Sets for Semiflows

In this short note, we are interested in discussing characteristics of finite generating sets for $\mathcal{F}$, the set of all semiflows with non negative coefficients of a Petri Net. By systematically positioning these results over semi rings such as $\mathbb{N}$ or $\mathbb{Q^+}$ then over a field such as $\mathbb{Q}$, we were able to discover a handful of new results

cs.FL↗

arXiv2020

A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks

Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wrong classification results. Such variability has been a potential risk for systems in real-life equipped DNNs as core components. Numerous efforts have been put into research on how to protect DNN models from being tackled by AEs. However, no previous work can efficiently reduce the effects caused by novel adversarial attacks and be compatible with real-life constraints at the same time. In this paper, we focus on developing a lightweight defense method that can efficiently invalidate full whitebox adversarial attacks with the compatibility of real-life constraints. From basic affine transformations, we integrate three transformations with randomized coefficients that fine-tuned respecting the amount of change to the defended sample. Comparing to 4 state-of-art defense methods published in top-tier AI conferences in the past two years, our method demonstrates outstanding robustness and efficiency. It is worth highlighting that, our model can withstand advanced adaptive attack, namely BPDA with 50 rounds, and still helps the target model maintain an accuracy around 80 %, meanwhile constraining the attack success rate to almost zero.

cs.CR↗

arXiv2020

Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques

Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the attackers and defenders. Recently, advanced gradient-based attack techniques were proposed (e.g., BPDA and EOT), which have defeated a considerable number of existing defense methods. Up to today, there are still no satisfactory solutions that can effectively and efficiently defend against those attacks. In this paper, we make a steady step towards mitigating those advanced gradient-based attacks with two major contributions. First, we perform an in-depth analysis about the root causes of those attacks, and propose four properties that can break the fundamental assumptions of those attacks. Second, we identify a set of operations that can meet those properties. By integrating these operations, we design two preprocessing functions that can invalidate these powerful attacks. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years. The defender can employ our solutions to constrain the attack success rate below 7% for the strongest attacks even the adversary has spent dozens of GPU hours.

cs.CR↗

arXiv2020

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transmitting fewer image data at the sender's end. At the receiver's end, we propose a DC recovery algorithm together with the deep residual learning framework to recover images with high quality. The second work is about adversarial examples defenses based on signal processing. We propose the wavelet extension method to extend image data features, which makes it more difficult to generate adversarial examples. We further adopt wavelet denoising to reduce the influence of the adversarial perturbations. With intensive experiments, we demonstrate that both works are effective in their application scenarios.

eess.IV↗

arXiv2020

Revisiting Shared Data Protection Against Key Exposure

This paper puts a new light on secure data storage inside distributed systems. Specifically, it revisits computational secret sharing in a situation where the encryption key is exposed to an attacker. It comes with several contributions: First, it defines a security model for encryption schemes, where we ask for additional resilience against exposure of the encryption key. Precisely we ask for (1) indistinguishability of plaintexts under full ciphertext knowledge, (2) indistinguishability for an adversary who learns: the encryption key, plus all but one share of the ciphertext. (2) relaxes the "all-or-nothing" property to a more realistic setting, where the ciphertext is transformed into a number of shares, such that the adversary can't access one of them. (1) asks that, unless the user's key is disclosed, noone else than the user can retrieve information about the plaintext. Second, it introduces a new computationally secure encryption-then-sharing scheme, that protects the data in the previously defined attacker model. It consists in data encryption followed by a linear transformation of the ciphertext, then its fragmentation into shares, along with secret sharing of the randomness used for encryption. The computational overhead in addition to data encryption is reduced by half with respect to state of the art. Third, it provides for the first time cryptographic proofs in this context of key exposure. It emphasizes that the security of our scheme relies only on a simple cryptanalysis resilience assumption for blockciphers in public key mode: indistinguishability from random, of the sequence of diferentials of a random value. Fourth, it provides an alternative scheme relying on the more theoretical random permutation model. It consists in encrypting with sponge functions in duplex mode then, as before, secret-sharing the randomness.

cs.CR↗

arXiv2019

Building the Case for Temperature Awareness in Energy Consumption Models: an Application of the Energy-Frequency Convexity Rule

Optimizing computing and communication systems that host energy-critical applications is becoming a key issue for software developers. In previous work, we introduced and validated the Energy/Frequency Convexity Rule for CPU-bound benchmarks on recent ARM platforms. This rule states that there exists an optimal clock frequency that minimizes the CPU's energy consumption for non-performance-critical programs. We showed that the Energy/Frequency Convexity Rule is related to the non-linearity of power with respect to frequency and is not dependent on the supply voltage. Here, we discuss the application of an analytical energy consumption model proposed previously to our target board, a TI AM572x EVM. We show that this non-linear analytical model can, for our experimental settings, be approximated by a frequency-linear variant, as our voltage is maintained constant. This, however, does not fit the measurements on the board, suggesting that a parameter is currently missing in the analytical model. We conjecture that accounting for temperature in the model would yield more accurate results that are in-line with our measurements. This builds the case for the inclusion of this important parameter in our energy models.

cs.AR↗

arXiv2019

Privacy-preserving Health Data Sharing for Medical Cyber-Physical Systems

The recent spades of cyber security attacks have compromised end users' data safety and privacy in Medical Cyber-Physical Systems (MCPS). Traditional standard encryption algorithms for data protection are designed based on a viewpoint of system architecture rather than a viewpoint of end users. As such encryption algorithms are transferring the protection on the data to the protection on the keys, data safety and privacy will be compromised once the key is exposed. In this paper, we propose a secure data storage and sharing method consisted by a selective encryption algorithm combined with fragmentation and dispersion to protect the data safety and privacy even when both transmission media (e.g. cloud servers) and keys are compromised. This method is based on a user-centric design that protects the data on a trusted device such as end user's smartphone and lets the end user to control the access for data sharing. We also evaluate the performance of the algorithm on a smartphone platform to prove the efficiency.

cs.CR↗

arXiv2019

Multi-hop Data Fragmentation in Unattended Wireless Sensor Networks

In this work, we analyze the advantages of multi-hop data fragmentation in unattended wireless sensor networks (UWSN) and propose a lightweight protocol to achieve it. UWSN has recently become an important aspect in various areas of sensor networks where real-time data collection is difficult to manage. However, the characteristics of UWSN also poses new problems especially in data protection. For more efficient protection, data fragmentation has been proposed to fragment sensing data, which prevents attackers from successfully exploiting the data. However, there are currently minimal work on the strategies of the placement of fragments inside a sensor network. Through this work, we analyze the effects of multi-hop fragment dispersal in relation to effectiveness of data protection and energy consumption. Furthermore, we design a new routing algorithm suitable for the energy-efficient placement of data fragments in UWSN. We utilize simulation-based modeling and testbed implementation via FIT/IoT-Lab to prove the effectiveness of our work.

cs.NI↗

arXiv2018

PE-AONT: Partial Encryption combined with an All-or-Nothing Transform

In this report, we introduce PE-AONT: a novel algorithm for fast and secure data fragmentation. Initial data are fragmented and only a selected subset of the fragments is encrypted. Further, fragments are transformed using a variation of an all-or-nothing transform that blends encrypted and non-encrypted fragments. By encrypting data only partially, we achieve better performance than relevant techniques including data encryption and straightforward fragmentation. Moreover, when the ratio between the number of encrypted and non-encrypted fragments is wisely chosen, data inside fragments are protected against exposure of the encryption key unless all fragments are gathered by an attacker.

cs.CR↗

arXiv2018

A Fast Fragmentation Algorithm For Data Protection In a Multi-Cloud Environment

Data fragmentation and dispersal over multiple clouds is a way of data protection against honest-but-curious storage or service providers. In this paper, we introduce a novel algorithm for data fragmentation that is particularly well adapted to be used in a multi-cloud environment. An empirical security analysis was performed on data sets provided by a large enterprise and shows that the scheme achieves good data protection. A performance comparison with published related works demonstrates it can be more than twice faster than the fastest of the relevant fragmentation techniques, while producing reasonable storage overhead.

cs.CR↗

arXiv2017

Data protection by means of fragmentation in various different distributed storage systems - a survey

This paper analyzes various distributed storage systems that use data fragmentation and dispersal as a way of protection.Existing solutions have been organized into two categories: bitwise and structurewise. Systems from the bitwise category are operating on unstructured data and in a uniform environment. Those having structured input data with predefined confidentiality level and disposing of a heterogeneous environment in terms of machine trustworthiness were classified as structurewise. Furthermore, we outline high-level requirements and desirable architecture traits of an eficient data fragmentation system, which will address performance (including latency), availability, resilience and scalability.

cs.CR↗

arXiv2017

An Efficient Keyless Fragmentation Algorithm for Data Protection

The family of Information Dispersal Algorithms is applied to distributed systems for secure and reliable storage and transmission. In comparison with perfect secret sharing it achieves a significantly smaller memory overhead and better performance, but provides only incremental confidentiality. Therefore, even if it is not possible to explicitly reconstruct data from less than the required amount of fragments, it is still possible to deduce some information about the nature of data by looking at preserved data patterns inside a fragment. The idea behind this paper is to provide a lightweight data fragmentation scheme, that would combine the space efficiency and simplicity that could be find in Information Dispersal Algorithms with a computational level of data confidentiality.

cs.CR↗

arXiv2017

Data Protection: Combining Fragmentation, Encryption, and Dispersion, a final report

Hardening data protection using multiple methods rather than 'just' encryption is of paramount importance when considering continuous and powerful attacks in order to observe, steal, alter, or even destroy private and confidential information.Our purpose is to look at cost effective data protection by way of combining fragmentation, encryption, and dispersion over several physical machines. This involves deriving general schemes to protect data everywhere throughout a network of machines where they are being processed, transmitted, and stored during their entire life cycle. This is being enabled by a number of parallel and distributed architectures using various set of cores or machines ranging from General Purpose GPUs to multiple clouds. In this report, we first present a general and conceptual description of what should be a fragmentation, encryption, and dispersion system (FEDS) including a number of high level requirements such systems ought to meet. Then, we focus on two kind of fragmentation. First, a selective separation of information in two fragments a public one and a private one. We describe a family of processes and address not only the question of performance but also the questions of memory occupation, integrity or quality of the restitution of the information, and of course we conclude with an analysis of the level of security provided by our algorithms. Then, we analyze works first on general dispersion systems in a bit wise manner without data structure consideration; second on fragmentation of information considering data defined along an object oriented data structure or along a record structure to be stored in a relational database.

cs.CR↗