Revisiting Content Availability in Distributed Online Social Networks

Online Social Networks (OSN) are among the most popular applications in today's Internet. Decentralized online social networks (DOSNs), a special class of OSNs, promise better privacy and autonomy than traditional centralized OSNs. However, ensuring availability of content when the content owner is not online remains a major challenge. In this paper, we rely on the structure of the social graphs underlying DOSN for replication. In particular, we propose that friends, who are anyhow interested in the content, are used to replicate the users content. We study the availability of such natural replication schemes via both theoretical analysis as well as simulations based on data from OSN users. We find that the availability of the content increases drastically when compared to the online time of the user, e. g., by a factor of more than 2 for 90% of the users. Thus, with these simple schemes we provide a baseline for any more complicated content replication scheme.Comment: 11pages, 12 figures; Technical report at TU Berlin, Department of Electrical Engineering and Computer Science (ISSN 1436-9915

On the relevance of APIs facing fairwashed audits

Recent legislation required AI platforms to provide APIs for regulators to assess their compliance with the law. Research has nevertheless shown that platforms can manipulate their API answers through fairwashing. Facing this threat for reliable auditing, this paper studies the benefits of the joint use of platform scraping and of APIs. In this setup, we elaborate on the use of scraping to detect manipulated answers: since fairwashing only manipulates API answers, exploiting scraps may reveal a manipulation. To abstract the wide range of specific API-scrap situations, we introduce a notion of proxy that captures the consistency an auditor might expect between both data sources. If the regulator has a good proxy of the consistency, then she can easily detect manipulation and even bypass the API to conduct her audit. On the other hand, without a good proxy, relying on the API is necessary, and the auditor cannot defend against fairwashing. We then simulate practical scenarios in which the auditor may mostly rely on the API to conveniently conduct the audit task, while maintaining her chances to detect a potential manipulation. To highlight the tension between the audit task and the API fairwashing detection task, we identify Pareto-optimal strategies in a practical audit scenario. We believe this research sets the stage for reliable audits in practical and manipulation-prone setups.Comment: 18 pages, 7 figure

Centralité du second ordre : Calcul distribué de l'importance de noeuds dans un réseau complexe

A complex network can be modeled as a graph representing the "who knows who" relationship. In the context of graph theory for social networks, the notion of centrality is used to assess the relative importance of nodes in a given network topology. For example, in a network composed of large dense clusters connected through only a few links, the nodes involved in those links are particularly critical as far as the network survivability is concerned. This may also impact any application running on top of it. Such information can be exploited for various topological maintenance issues to prevent congestion and disruptance. This can also be used offline to identify the most important nodes in large social interaction graphs. Several forms of centrality have been proposed so far. Yet, they suffer from imperfections : designed for abstract graphs, they are either of limited use (degree centrality), either uncomputable in a distributed setting (random walk betweenness centrality). In this paper we introduce a novel form of centrality : the second order centrality which can be computed in a fully decentralized manner. This provides locally each node with its relative criticity and relies on a random walk visiting the network in an unbiased fashion. To this end, each node records the time elapsed between visits of that random walk (called return time in the sequel) and computes the standard deviation (or second order moment) of such return times. Both through theoretical analysis and simulation, we show that the standard deviation can be used to accurately identify critical nodes as well as to globally characterize graphs topology in a fully decentralized way

Lymphopenia combined with low TCR diversity (divpenia) predicts poor overall survival in metastatic breast cancer patients

Lymphopenia (< 1Giga/L) detected before initiation of chemotherapy is a predictive factor for death in metastatic solid tumors. Combinatorial T cell repertoire (TCR) diversity was investigated and tested either alone or in combination with lymphopenia as a prognostic factor at diagnosis for overall survival (OS) in metastatic breast cancer (MBC) patients. The combinatorial TCR diversity was measured by semi quantitative multi-N-plex PCR on blood samples before the initiation of the first line chemotherapy in a development (n = 66) and validation (n = 67) MBC patient cohorts. A prognostic score, combining lymphocyte count and TCR diversity was evaluated. Univariate and multivariate analyses of prognostic factors for OS were performed in both cohorts. Lymphopenia and severe restriction of TCR diversity called “divpenia” (diversity ≤ 33%) were independently associated with shorter OS. Lympho-divpenia combining lymphopenia and severe divpenia accurately identified patients with poor OS in both cohorts (7.6 and 10.6 vs 24.5 and 22.9 mo). In multivariate analysis including other prognostic clinical factors, lympho-divpenia was found to be an independent prognostic factor in the pooled cohort (p = 0.005) along with lack of HER2 and hormonal receptors expression (p = 0.011) and anemia (p = 0.009). Lympho-divpenia is a novel prognostic factor that will be used to improve quality of MBC patients’ medical care

A Generic Trust Framework For Large-Scale Open Systems Using Machine Learning

In many large-scale distributed systems and on the Web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable interaction environment. A traditional approach to reason about the risk of a transaction is to determine if the involved agent is trustworthy on the basis of its behavior history. As a departure from such traditional trust models, we propose a generic, trust framework based on machine learning where an agent uses its own previous transactions (with other agents) to build a personal knowledge base. This is used to assess the trustworthiness of a transaction on the basis of the associated features, particularly using the features that help discern successful transactions from unsuccessful ones. These features are handled by applying appropriate machine learning algorithms to extract the relationships between the potential transaction and the previous ones. Experiments based on real data sets show that our approach is more accurate than other trust mechanisms, especially when the information about past behavior of the specific agent is rare, incomplete, or inaccurate

Low-Cost Secret-Sharing in Sensor Networks

International audienceRadio waves are the medium used by sensors to communicate and exchange data. The unconstrained accessibility to any information carried over this medium is a security issue in many sensor-based applications. Ensuring protected wireless communications is a problem that has received a lot of attention in the context of ad hoc networks. However, due to hardware constraints of sensors along with multi-hop communication, most of these solutions turn out to be useless for sensor networks. This paper provides basic building blocks to establish secure communication by exchanging secret keys between neighbor nodes without any use of cryptography methods allowing an gain in efficiency. This paper also proposes a second algorithm that extends the secret key establishment to nodes that are not direct neighbors. Among the interesting features of the proposed algorithms we can note a low overhead and the absence of initial configuration.Cet article présente un technique simple qui permet de sécuriser les communications dans un réseau de capteurs. Dans le contexte des réseaux de capteurs, la limitation au niveau des ressources (puissance de calcul, eńergie, communication) fait que l'usage de la cryptographie asymétrique classique n'est pas envisageable. La technique proposée repose sur le paradoxe des anniversaires appliqué au voisinage de chaque capteurs

Request Complexity of VNet Topology Extraction: Dictionary-Based Attacks

The network virtualization paradigm envisions an Internet where arbitrary virtual networks (VNets) can be specified and embedded over a shared substrate (e.g., the physical infrastructure). As VNets can be requested at short notice and for a desired time period only, the paradigm enables a flexible service deployment and an efficient resource utilization. This paper investigates the security implications of such an architecture. We consider a simple model where an attacker seeks to extract secret information about the substrate topology, by issuing repeated VNet embedding requests. We present a general framework that exploits basic properties of the VNet embedding relation to infer the entire topology. Our framework is based on a graph motif dictionary applicable for various graph classes. Moreover, we provide upper bounds on the request complexity, the number of requests needed by the attacker to succeed

Beyond San Fancisco Cabs : Building a *-lity Mining Dataset for Social Traces Analysis

International audienceIn this paper, we report our advances, choices and first insights in the design of a mobile phone powered data collection platform. We believe that collecting such data is vital to achieve a better understanding/modeling of several phe-nomenons related to human activity (e.g. mobility, social contacts, or terminal failures). However, designing such a platform raised a lot of questions discussed in this paper

Upper and lower bounds for deterministic broadcast in powerline communication networks

International audiencePowerline communication networks assume an interesting position in the communication network space: Similarly to wireless networks, powerline networks are based on a shared broadcast medium; unlike wireless networks , however, the signal propagation is constrained to the power lines of the electrical infrastructure, which is essentially a graph. This article presents an algorithmic model to study the design of communication services over powerline communication networks. As a case study, we focus on the fundamental broadcast problem, and present and analyze a distributed algorithm COLORCAST which terminates in at most n communication rounds, where n denotes the network size, even in a model where link qualities are unpredictable and time-varying. For comparison, the achieved broadcast time is lower than what can be achieved by any unknown-topology algorithm (lower bounds Ω(n log n/ log(n/D)) and Ω(n log D) are proved in [22] resp. [10] where D is the network diameter). Moreover, existing known-topology broadcast algorithms often fail to deliver the broadcast message entirely in this model. This article also presents a general broadcast lower bound for the powerline model