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EAEPE Young Scholars Pre-Conference 2017


29th Annual Conference of the European Association for Evolutionary Political Economy
 
Budapest, Hungary: 19-21 October 2017
 
Pre-Conference: 18-19 October 2017

ALAPKÉPZÉS

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The 5th From Past to Future with Human and Social Sciences

(HASSACC 2017)

Conference dates: September 25 - 29, 2017

MAGISZTERI

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The 5th From Past to Future with Human and Social Sciences

(HASSACC 2017)

Conference dates: September 25 - 29, 2017

Phd / DLA

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The 5th From Past to Future with Human and Social Sciences

(HASSACC 2017)

Conference dates: September 25 - 29, 2017

POSZTDOKTORI

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The 5th From Past to Future with Human and Social Sciences

(HASSACC 2017)

Conference dates: September 25 - 29, 2017

FIATAL KUTATÓI

Közeli határidők

The 5th From Past to Future with Human and Social Sciences

(HASSACC 2017)

Conference dates: September 25 - 29, 2017

OKTATÓI


Jelenlegi oktatási szint:
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KÉPZÉSI SZINT
Alapképzés
Magiszteri
PhD / DLA
Posztdoktori
Fiatal kutatói
Oktatói
Kutatói
Doktorjelölt
Frissen végzett
TUDOMÁNYTERÜLET
FELHÍVÁS TÍPUSA
CÉLORSZÁG
Szűrő
Megszüntet

Postdoc, Privacy Preserving Data Mining in P2P Networks


Privacy preserving data mining in P2P environments for smart users' communities.
Peer-to-Peer (P2P) systems provide major advantages in terms of scalability, autonomy and dynamic behavior of peers, and data distribution.

The P2P model stands in direct contrast to  the traditional client-server model, as it introduces symmetry in roles, where each peer is both a client and a server. Whereas a client-server network requires more investment to serve more clients, a P2P network pools the resources of each peer for the common good. In other terms, it exhibits the network effect as defined by economists: the value of a network to an individual user scales with the total number of participants. Thus, P2P networks are naturally suited for handling large-scale applications, due to their inherent self-scalability.

The research context of this work is time series analysis on P2P networks. Typically, distributed P2P data oriented applications requires users to store their own data, and to run specific tasks on this data. In most cases, a peer may hold information related to the user's profile, behavior, etc, with respect to a specific application.

In addition, user data is commonly updated continuously and, after a time period, or a specific amount of data processing tasks, users data may be exchanged or collected to accomplish some critical analysis. In our context, each peer owns a (set of) time series related to sensitive information that must remain confidential. Meanwhile, data owners (peers) want to analyze their data by comparing it to the time series of other peers, and the group of peers in the network want to participate to a knowledge discovery process over the data of the whole network.

The work will be done in the Zenith Team of INRIA, held by the LIRMM Laboratory (Montpellier, south of France). 
It will be supervised by members of Zenith (Florent Masseglia and Esther Pacitti) and EDF R&D (Georges Hebrail).

The ideal applicant has:
- A Ph.D. in computer science or mathematics (mandatory).
- Experience in privacy preserving methods or cryptography (mandatory).
- Experience in time series management.
- Proficiency in English.
- Strong knowledge of C++ or Java.

Duration is 12 months. The net salary is 2138 Euros and includes social security (gross salary is EUR 2620.84).
The position should be fulfilled by September 2012 (however, a starting date by December 2012 may be negotiated).
Contact: Florent Masseglia -  florent.masseglia@inria.fr and Esther Pacitti - Esther.Pacitti@lirmm.fr
Zenith Web pagewww-sop.inria.fr/zenith

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