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Patrick Loiseau

Publications and source records attributed to Patrick Loiseau.

49 records · Page 3Linked to original sources

Forgetting the Forgotten with Letheia, Concealing Content Deletion from Persistent Observers

Most social platforms offer mechanisms allowing users to delete their posts, and a significant fraction of users exercise this right to be forgotten. However, ironically, users' attempt to reduce attention to sensitive posts via deletion, in practice, attracts unwanted attention from stalkers specifically to those posts. Thus, deletions may leave users more vulnerable to attacks on their privacy in general. Users hoping to make their posts forgotten face a "damned if I do, damned if I don't" dilemma. Many are shifting towards ephemeral social platform like Snapchat, which will deprive us of important user-data archival. In the form of intermittent withdrawals, we present, Lethe, a novel solution to this problem of forgetting the forgotten. If the next-generation social platforms are willing to give up the uninterrupted availability of non-deleted posts by a very small fraction, Lethe provides privacy to the deleted posts over long durations. In presence of Lethe, an adversarial observer becomes unsure if some posts are permanently deleted or just temporarily withdrawn by Lethe; at the same time, the adversarial observer is overwhelmed by a large number of falsely flagged undeleted posts. To demonstrate the feasibility and performance of Lethe, we analyze large-scale real data about users' deletion over Twitter and thoroughly investigate how to choose time duration distributions for alternating between temporary withdrawals and resurrections of non-deleted posts. We find a favorable trade-off between privacy, availability and adversarial overhead in different settings for users exercising their right to delete. We show that, even against an ultimate adversary with an uninterrupted access to the entire platform, Lethe offers deletion privacy for up to 3 months from the time of deletion, while maintaining content availability as high as 95% and keeping the adversarial precision to 20%.

cs.CR↗

Algorithms for Scheduling Malleable Cloud Tasks

Due to the ubiquity of batch data processing in cloud computing, the related problem of scheduling malleable batch tasks and its extensions have received significant attention recently. In this paper, we consider a fundamental model where a set of n tasks is to be processed on C identical machines and each task is specified by a value, a workload, a deadline and a parallelism bound. Within the parallelism bound, the number of machines assigned to a task can vary over time without affecting its workload. For this model, we obtain two core results: a sufficient and necessary condition such that a set of tasks can be finished by their deadlines on C machines, and an algorithm to produce such a schedule. These core results provide a conceptual tool and an optimal scheduling algorithm that enable proposing new algorithmic analysis and design and improving existing algorithms under various objectives.

cs.DC↗

A Game-Theoretic Analysis of Adversarial Classification

Attack detection is usually approached as a classification problem. However, standard classification tools often perform poorly because an adaptive attacker can shape his attacks in response to the algorithm. This has led to the recent interest in developing methods for adversarial classification, but to the best of our knowledge, there have been very few prior studies that take into account the attacker's tradeoff between adapting to the classifier being used against him with his desire to maintain the efficacy of his attack. Including this effect is key to derive solutions that perform well in practice. In this investigation we model the interaction as a game between a defender who chooses a classifier to distinguish between attacks and normal behavior based on a set of observed features and an attacker who chooses his attack features (class 1 data). Normal behavior (class 0 data) is random and exogenous. The attacker's objective balances the benefit from attacks and the cost of being detected while the defender's objective balances the benefit of a correct attack detection and the cost of false alarm. We provide an efficient algorithm to compute all Nash equilibria and a compact characterization of the possible forms of a Nash equilibrium that reveals intuitive messages on how to perform classification in the presence of an attacker. We also explore qualitatively and quantitatively the impact of the non-attacker and underlying parameters on the equilibrium strategies.

cs.GT↗

Dissecting demand response mechanisms: the role of consumption forecasts and personalized offers

Demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to rearrange their consumption in order to reduce production costs, are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users. In this paper, we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the different mechanisms and their sensitivity to forecast errors. At a high level, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than

math.OC↗

Improved Competitive Analysis of Online Scheduling Deadline-Sensitive Jobs

We consider the following scheduling problem. There is a single machine and the jobs will arrive for completion online. Each job j is preemptive and, upon its arrival, its other characteristics are immediately revealed to the machine: the deadline requirement, the workload and the value. The objective is to maximize the aggregate value of jobs completed by their deadlines. Using the minimum of the ratios of deadline minus arrival time to workload over all jobs as the slackness s, a non-committed and a committed online scheduling algorithm is proposed in [Lucier et al., SPAA'13; Azar et al., EC'15], achieving competitive ratios of 2+f(s), where the big O notation f(s)=\mathcal{O}(\frac{1}{(\sqrt[3]{s}-1)^{2}}), and (2+f(s*b))/b respectively, where b=ω*(1-ω), ωis in (0, 1), and s is no less than 1/b. In this paper, without recourse to the dual fitting technique used in the above works, we propose a simpler and more intuitive analytical framework for the two algorithms, improving the competitive ratio of the first algorithm by 1 and therefore improving the competitive ratio of the second algorithm by 1/b. As stated in [Lucier et al., SPAA'13; Azar et al. EC'15], it is justifiable in scenarios like the online batch processing for cloud computing that the slackness s is large, hence the big O notation in the above competitive ratios can be ignored. Under the assumption, our analysis brings very significant improvements to the competitive ratios of the two algorithms: from 2 to 1 and from 2/b to 1/b respectively.

cs.DS↗

On the Reliability of Profile Matching Across Large Online Social Networks

Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has proposed methods to match profiles and showed that it is possible to do it accurately, but using evaluations that focused on sampled datasets only. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios.

cs.SI↗

A Game-Theoretic Study on Non-Monetary Incentives in Data Analytics Projects with Privacy Implications

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoff scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibria that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

cs.GT↗

Causal study of Network Performance

The use of Internet in the every day life has pushed its evolution in a very fast way. The heterogeneity of the equipments supporting its networks, as well as the different devices from which it can be accessed, have participated in increasing the complexity of understanding its global behavior and performance. In our study we propose a new method for studying the performance of TCP protocol based on causal graphs. Causal graphs offer models easy to interpret and use. They highlight the structural model of the system they represent and give us access to the causal dependences between the different parameters of the system. One of the major contribution of causal graphs is their ability to predict the effects of an intervention from observations made before this intervention.

cs.NI↗

Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate versus Time-of-Day Pricing

Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a \emph{fixed-budget rebate mechanism} that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the \emph{public good} nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases.

cs.NI↗

Un modèle de trafic adapté à la volatilité de charge d'un service de vidéo à la demande: Identification, validation et application à la gestion dynamique de ressources

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this report we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. As an essential step we also derive a heuristic identification procedure to calibrate all the model parameters and evaluate the performance of our estimator on synthetic time series. We show how good can our model fit to real workload traces with respect to the stationary case in terms of steady-state probability and autocorrelation structure. We find that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.

cs.NI↗

Dynamic Resource Management in Clouds: A Probabilistic Approach

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz/flash crowd effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking

cs.NI↗

A Game-Theoretical Approach for Finding Optimal Strategies in an Intruder Classification Game

We consider a game in which a strategic defender classifies an intruder as spy or spammer. The classification is based on the number of file server and mail server attacks observed during a fixed window. The spammer naively attacks (with a known distribution) his main target: the mail server. The spy strategically selects the number of attacks on his main target: the file server. The defender strategically selects his classification policy: a threshold on the number of file server attacks. We model the interaction of the two players (spy and defender) as a nonzero-sum game: The defender needs to balance missed detections and false alarms in his objective function, while the spy has a tradeoff between attacking the file server more aggressively and increasing the chances of getting caught. We give a characterization of the Nash equilibria in mixed strategies, and demonstrate how the Nash equilibria can be computed in polynomial time. Our characterization gives interesting and non-intuitive insights on the players' strategies at equilibrium: The defender uniformly randomizes between a set of thresholds that includes very large values. The strategy of the spy is a truncated version of the spammer's distribution. We present numerical simulations that validate and illustrate our theoretical results.

cs.GT↗

Large deviations for the local fluctuations of random walks and new insights into the "randomness" of Pi

We establish large deviations properties valid for almost every sample path of a class of stationary mixing processes $(X_1,..., X_n,...)$. These properties are inherited from those of $S_n=\sum_{i=1}^nX_i$ and describe how the local fluctuations of almost every realization of $S_n$ deviate from the almost sure behavior. These results apply to the fluctuations of Brownian motion, Birkhoff averages on hyperbolic dynamics, as well as branching random walks. Also, they lead to new insights into the "randomness" of the digits of expansions in integer bases of Pi. We formulate a new conjecture, supported by numerical experiments, implying the normality of Pi.

math.PR↗