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Privacy Leakage of Search-based Multi-agent Planning Algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00358677" target="_blank" >RIV/68407700:21230/22:00358677 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/s10458-022-09568-4" target="_blank" >https://doi.org/10.1007/s10458-022-09568-4</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10458-022-09568-4" target="_blank" >10.1007/s10458-022-09568-4</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Privacy Leakage of Search-based Multi-agent Planning Algorithms

  • Original language description

    Privacy preservation has become one of the crucial research topics in multi-agent planning. A number of techniques to preserve private information throughout the planning process have emerged. One major difficulty of such research is the comparison of properties related to privacy among such techniques. A metric allowing for comparison of such privacy preservation was introduced only recently, having a number of drawbacks such as prohibitive computational complexity. In this work we strengthen the theoretical foundations and simplify the metric in order to be practically usable. Moreover, we test the usability of the metric in an analysis of various techniques in multi-agent heuristic computation and search, determining which are the most beneficial in terms of privacy preservation. We also evaluate the techniques in terms of the classical IPC score to assess their impact on the overall planning performance. The results are somewhat surprising and show that extracting any privacy-related information even from the simplest variant of heuristic search is a very complicated task. Existing techniques such as distributed heuristic and sending only relevant states is shown to reduce the privacy leakage even more.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GJ18-24965Y" target="_blank" >GJ18-24965Y: Privacy Preserving Multi-agent Planning</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Autonomous Agents and Multi-Agent Systems

  • ISSN

    1387-2532

  • e-ISSN

    1573-7454

  • Volume of the periodical

    36

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    32

  • Pages from-to

    1-32

  • UT code for WoS article

    000820223500001

  • EID of the result in the Scopus database

    2-s2.0-85133331223