Rania Talbi - Academia.edu (original) (raw)
Papers by Rania Talbi
"la langue est un systeme de systemes". Cette definition saussurienne de la langue perm... more "la langue est un systeme de systemes". Cette definition saussurienne de la langue permet de situer notre travail -le systeme des prepositions- et l'objet de notre travail, en l'occurrence la preposition espagnole, comme un probleme partiel et structurel de linguistique generale et comme un depassement de la ponctualite occurrentielle ou discursive de la preposition. L'apprehension systematique des prepositions n'est cependant pas un fait arbitraire puisque c'est l'existence meme d'un paradigme grammatical qui indique qu'il y a systeme, paradigme ne voulant toutefois pas dire systeme. L'inventaire plus ou moins determine des signifiants constitutifs -premier probleme pose par la preposition- implique donc un systeme en puissance et des principes organisateurs entre les signifiants. Ces principes systematiques et integrateurs supposent egalement une "mise en relation" des prepositions, d'ou notre refus de reduire l'objet d'etude ou de choisir entre l'une et l'autre preposition. La difficulte qui s'impose a nous lorsqu'il s'agit de definir la signification d'une preposition releve de la nature meme de ce mot auquel il est demande de signifier, de "designer" plusieurs choses. Ce signifiant s'inscrit en effet dans le systeme grammatical et dans le systeme lexicologique puisqu'il designe a la fois une "notion" (en aucun cas un referent experienciel) et une fonction. Cette double designation est essentiellement relationnelle : la preposition, comme mot diastematique "nomme" les relations syntaxiques et semantiques qu'entretiennent les mots entre eux et elle "nomme" une semantesede nature relationnelle qui trouve son application dans l'un des trois champs operationnels suivants : spatial, temporel et notionnel. Cette semantese invariable dans le systeme prepositionnel "realise" sa particularite et sa singularite expressive et discursive dans chacun de ses emplois. La simple actualisation d'une preposition retablit le rapport referentiel entre deux supports et marque un cas de discours analytique par opposition au cas de discours synthetique -sans preposition-. Ce statut demarcatif et incidentiel "ont fait dire" a certains linguistes que la preposition n'ajoutait rien a la signification, au contenu d'un enonce et qu'elle n'etait au niveau du syntagme et de la phrase que nullement ou faiblement significative, c'est pourtant le relateur qui annonce une variation info
Le Centre pour la Communication Scientifique Directe - HAL - Inria, Jul 5, 2021
Federated learning is a new machine learning trend that, guided by privacy goals, distributes lea... more Federated learning is a new machine learning trend that, guided by privacy goals, distributes learning across multiple participants who train the model collaboratively without sharing their data. Nonetheless, it is vulnerable to a variety of attacks such as data and model poisoning. In these attacks, adversaries attempt to inject a backdoor task in the model along with its main task during the training phase. After that, the injected backdoor is exploited at inference-time given a specific trigger. Many state-of-the-art mechanisms that rely on model update auditing have been proposed to mitigate poisoning attacks. We show in this paper that attackers are still capable to evade such detectors by crafting model updates that mimic benign ones. In this paper, we propose ARMOR, a novel mechanism that successfully detects these backdoor attacks in Federated Learning. We describe the design principles of ARMOR based on generative adversarial networks. And we present ARMOR's evaluation results on a real world dataset, which demonstrates that it outperforms its competitors.
2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S)
Proceedings of the 19th International Middleware Conference Industry, 2018
With the wide adoption of the Internet, digital transactions surge exponentially and so do the im... more With the wide adoption of the Internet, digital transactions surge exponentially and so do the impersonation fraud. While machine learning techniques show strong promise to be the building block for digital fraud detection systems, clients may be reluctant to share the raw data with such systems due to privacy concerns. The emerging privacy preserving machine learning techniques that employ homomorphic encryption to resolve this conundrum unfortunately increases the computational overhead of detection. In this paper, we present a first-of-a-kind empirical performance study of a private fraud detection system developed at SiS ID, a French business security platform. A privacy-preserving decision tree which can classify transactions into four risk classes (safe, moderately risky, very risky and fraud) is trained on more than 160000 real world transactions, and we quantitatively compare the classification accuracy, latency and network bandwidth under various combinations of encryption parameters and learning hyper-parameters, in order to explore the impact of the configuration on the performances. Our results show that the computation and communication overhead of processing encrypted data increases by an order of magnitude of 5, and highly depends on the configuration of the encryption key and the number of nodes in the decision tree.
2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2020
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of ... more Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose lowquality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Towards Scalable, Efficient and Privacy Preserving Machine Learning Rania Talbi, Sara Bouchenak
Nowadays, machine learning algorithms are used in a myriad of domains, such as medical diagnosis,... more Nowadays, machine learning algorithms are used in a myriad of domains, such as medical diagnosis, fraud detection and user behavior analysis. However, in some of these cases, it is important that the data manipulated by these algorithms remain confidential. In order to address this paradox, the domain of privacy preserving machine learning has emerged. In this work, we describe the design principles of an outsourced incremental data classification scheme that satisfies this privacy constraint, while maintaining good classification accuracy and computational performance. 1 Context and Research Problem Statement With the pervasiveness of computer devices and digital services, huge amounts of data are nowadays continuously generated and collected. Machine learning is an increasingly popular set of tools which are used to extract hidden yet valuable knowledge from this data. These tools can be quite beneficial in many application domains, such as medical diagnosis, fraud detection, user...
2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2018
In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification s... more In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification service. It allows incremental decision tree learning over encrypted training data continuously sent by multiple data owners, without having access to the actual content of this data. In the same time, the learnt classification model is used to respond to encrypted classification queries while preserving the privacy of the query, the output corresponding to it and the model itself.
Herpetozoa
We present life history notes on two captive (observed during summer and late autumn) and on four... more We present life history notes on two captive (observed during summer and late autumn) and on fourteen, randomly encountered free-ranging specimens of Platyceps elegantissimus (GÜNTHER, 1878). Activity patterns of the captive specimens were bimodal, their morning activity peaking between 10:00 and 11:00 both in summer and autumn. The afternoon activity peaked between 18:00 and 19:00 in summer and between 14:00 and 15:00 in autumn. Nine among the 14 wild-ranging specimens encountered were observed in daylight, one at dusk and two in full darkness (early morning and late evening), two were inactive when found in their shelters during the day. Captive specimens immediately noticed any movement around the terrarium, reacting by quick flight or by freezing their body. One captive and one wild-ranging specimen were observed basking in the morning. Contrary to widely accepted views that P. elegantissimus is nocturnal, it conducts a predominantly diurnal, although cryptic lifestyle.
La Tunisie médicale, 1999
ABSTRACT We present life history notes on two captive (observed during summer and late autumn) an... more ABSTRACT We present life history notes on two captive (observed during summer and late autumn) and on fourteen, occasionally encountered free-ranging specimens of Coluber elegantissimus (Guenther, 1878). Activity patterns of the captive specimens were bimodal, their morning activity peaking between 10.00 and 11.00 both in summer and autumn. The adfternoon activity peaked between 18.00 and 19.00 in summer and between 14.00 and 15.00 in autumn. Nine among the 14 wild-ranging specimens encountered were observed in daylight, one at dusk and two in full darkness (early morning and late evening), two were inactive when found in their shelters during the day. Captive specimens immediately noticed any movement around the terrarium, reacting by quick flight or freezing their body. One captive and one wild-ranging specimen were observed basking in the morning. Contrary to widely accepted views that C. elegantissimus is nocturnal, it conducts a predominantly diurnal, although cryptic lifestyle.
"la langue est un systeme de systemes". Cette definition saussurienne de la langue perm... more "la langue est un systeme de systemes". Cette definition saussurienne de la langue permet de situer notre travail -le systeme des prepositions- et l'objet de notre travail, en l'occurrence la preposition espagnole, comme un probleme partiel et structurel de linguistique generale et comme un depassement de la ponctualite occurrentielle ou discursive de la preposition. L'apprehension systematique des prepositions n'est cependant pas un fait arbitraire puisque c'est l'existence meme d'un paradigme grammatical qui indique qu'il y a systeme, paradigme ne voulant toutefois pas dire systeme. L'inventaire plus ou moins determine des signifiants constitutifs -premier probleme pose par la preposition- implique donc un systeme en puissance et des principes organisateurs entre les signifiants. Ces principes systematiques et integrateurs supposent egalement une "mise en relation" des prepositions, d'ou notre refus de reduire l'objet d'etude ou de choisir entre l'une et l'autre preposition. La difficulte qui s'impose a nous lorsqu'il s'agit de definir la signification d'une preposition releve de la nature meme de ce mot auquel il est demande de signifier, de "designer" plusieurs choses. Ce signifiant s'inscrit en effet dans le systeme grammatical et dans le systeme lexicologique puisqu'il designe a la fois une "notion" (en aucun cas un referent experienciel) et une fonction. Cette double designation est essentiellement relationnelle : la preposition, comme mot diastematique "nomme" les relations syntaxiques et semantiques qu'entretiennent les mots entre eux et elle "nomme" une semantesede nature relationnelle qui trouve son application dans l'un des trois champs operationnels suivants : spatial, temporel et notionnel. Cette semantese invariable dans le systeme prepositionnel "realise" sa particularite et sa singularite expressive et discursive dans chacun de ses emplois. La simple actualisation d'une preposition retablit le rapport referentiel entre deux supports et marque un cas de discours analytique par opposition au cas de discours synthetique -sans preposition-. Ce statut demarcatif et incidentiel "ont fait dire" a certains linguistes que la preposition n'ajoutait rien a la signification, au contenu d'un enonce et qu'elle n'etait au niveau du syntagme et de la phrase que nullement ou faiblement significative, c'est pourtant le relateur qui annonce une variation info
Le Centre pour la Communication Scientifique Directe - HAL - Inria, Jul 5, 2021
Federated learning is a new machine learning trend that, guided by privacy goals, distributes lea... more Federated learning is a new machine learning trend that, guided by privacy goals, distributes learning across multiple participants who train the model collaboratively without sharing their data. Nonetheless, it is vulnerable to a variety of attacks such as data and model poisoning. In these attacks, adversaries attempt to inject a backdoor task in the model along with its main task during the training phase. After that, the injected backdoor is exploited at inference-time given a specific trigger. Many state-of-the-art mechanisms that rely on model update auditing have been proposed to mitigate poisoning attacks. We show in this paper that attackers are still capable to evade such detectors by crafting model updates that mimic benign ones. In this paper, we propose ARMOR, a novel mechanism that successfully detects these backdoor attacks in Federated Learning. We describe the design principles of ARMOR based on generative adversarial networks. And we present ARMOR's evaluation results on a real world dataset, which demonstrates that it outperforms its competitors.
2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S)
Proceedings of the 19th International Middleware Conference Industry, 2018
With the wide adoption of the Internet, digital transactions surge exponentially and so do the im... more With the wide adoption of the Internet, digital transactions surge exponentially and so do the impersonation fraud. While machine learning techniques show strong promise to be the building block for digital fraud detection systems, clients may be reluctant to share the raw data with such systems due to privacy concerns. The emerging privacy preserving machine learning techniques that employ homomorphic encryption to resolve this conundrum unfortunately increases the computational overhead of detection. In this paper, we present a first-of-a-kind empirical performance study of a private fraud detection system developed at SiS ID, a French business security platform. A privacy-preserving decision tree which can classify transactions into four risk classes (safe, moderately risky, very risky and fraud) is trained on more than 160000 real world transactions, and we quantitatively compare the classification accuracy, latency and network bandwidth under various combinations of encryption parameters and learning hyper-parameters, in order to explore the impact of the configuration on the performances. Our results show that the computation and communication overhead of processing encrypted data increases by an order of magnitude of 5, and highly depends on the configuration of the encryption key and the number of nodes in the decision tree.
2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2020
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of ... more Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose lowquality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Towards Scalable, Efficient and Privacy Preserving Machine Learning Rania Talbi, Sara Bouchenak
Nowadays, machine learning algorithms are used in a myriad of domains, such as medical diagnosis,... more Nowadays, machine learning algorithms are used in a myriad of domains, such as medical diagnosis, fraud detection and user behavior analysis. However, in some of these cases, it is important that the data manipulated by these algorithms remain confidential. In order to address this paradox, the domain of privacy preserving machine learning has emerged. In this work, we describe the design principles of an outsourced incremental data classification scheme that satisfies this privacy constraint, while maintaining good classification accuracy and computational performance. 1 Context and Research Problem Statement With the pervasiveness of computer devices and digital services, huge amounts of data are nowadays continuously generated and collected. Machine learning is an increasingly popular set of tools which are used to extract hidden yet valuable knowledge from this data. These tools can be quite beneficial in many application domains, such as medical diagnosis, fraud detection, user...
2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2018
In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification s... more In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification service. It allows incremental decision tree learning over encrypted training data continuously sent by multiple data owners, without having access to the actual content of this data. In the same time, the learnt classification model is used to respond to encrypted classification queries while preserving the privacy of the query, the output corresponding to it and the model itself.
Herpetozoa
We present life history notes on two captive (observed during summer and late autumn) and on four... more We present life history notes on two captive (observed during summer and late autumn) and on fourteen, randomly encountered free-ranging specimens of Platyceps elegantissimus (GÜNTHER, 1878). Activity patterns of the captive specimens were bimodal, their morning activity peaking between 10:00 and 11:00 both in summer and autumn. The afternoon activity peaked between 18:00 and 19:00 in summer and between 14:00 and 15:00 in autumn. Nine among the 14 wild-ranging specimens encountered were observed in daylight, one at dusk and two in full darkness (early morning and late evening), two were inactive when found in their shelters during the day. Captive specimens immediately noticed any movement around the terrarium, reacting by quick flight or by freezing their body. One captive and one wild-ranging specimen were observed basking in the morning. Contrary to widely accepted views that P. elegantissimus is nocturnal, it conducts a predominantly diurnal, although cryptic lifestyle.
La Tunisie médicale, 1999
ABSTRACT We present life history notes on two captive (observed during summer and late autumn) an... more ABSTRACT We present life history notes on two captive (observed during summer and late autumn) and on fourteen, occasionally encountered free-ranging specimens of Coluber elegantissimus (Guenther, 1878). Activity patterns of the captive specimens were bimodal, their morning activity peaking between 10.00 and 11.00 both in summer and autumn. The adfternoon activity peaked between 18.00 and 19.00 in summer and between 14.00 and 15.00 in autumn. Nine among the 14 wild-ranging specimens encountered were observed in daylight, one at dusk and two in full darkness (early morning and late evening), two were inactive when found in their shelters during the day. Captive specimens immediately noticed any movement around the terrarium, reacting by quick flight or freezing their body. One captive and one wild-ranging specimen were observed basking in the morning. Contrary to widely accepted views that C. elegantissimus is nocturnal, it conducts a predominantly diurnal, although cryptic lifestyle.