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Auflistung nach Schlagwort "k-Anonymity"

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  • Textdokument
    Achieving Facial De-Identification by Taking Advantage of the Latent Space of Generative Adversarial Networks
    (INFORMATIK 2021, 2021) Frick, Raphael Antonius; Steinebach, Martin
    The General Data Protection Regulation (EU)2016/679 passed by the European Union prohibits any data collection and processing that was conducted without the consent of the individuals involved. Especially images showing faces are often subject to these regulations and therefore, either need to be removed or anonymized. Early approaches however were often troubled by strong visual artifacts. In this work, we propose a novel anonymization pipeline that generates a proxy face for a group of individuals by taking advantage of the semantics of the latent space of generative adversarial networks. Experiments have shown that by following a 𝑘-same approach and utilizing different clustering techniques, privacy for the individuals involved can be greatly enhanced, while preserving important facial characteristics.
  • Konferenzbeitrag
    Improving Anonymization Clustering
    (SICHERHEIT 2018, 2018) Thaeter, Florian; Reischuk, Rüdiger
    Microaggregation is a technique to preserve privacy when confidential information about individuals shall be used by third parties. A basic property to be established is called k-anonymity. It requires that identifying information about individuals should not be unique, instead there has to be a group of size at least k that looks identical. This is achieved by clustering individuals into appropriate groups and then averaging the identifying information. The question arises how to select these groups such that the information loss by averaging is minimal. This problem has been shown to be NP-hard. Thus, several heuristics called MDAV, V-MDAV,... have been proposed for finding at least a suboptimal clustering. This paper proposes a more sophisticated, but still efficient strategy called MDAV* to construct a good clustering. The question whether to extend a group locally by individuals close by or to start a new group with such individuals is investigated in more depth. This way, a noticeable lower information loss can be achieved which is shown by applying MDAV* to several established benchmarks of real data and also to specifically designed random data.