Web11 jan. 2024 · Attacks against split learning are an important evaluation tool and have been receiving increased research attention recently. This work's contribution is applying a recent feature space hijacking attack (FSHA) to the learning process of a split neural network enhanced with differential privacy (DP), using a client-side off-the-shelf DP optimizer. Web22 aug. 2010 · However, up to now, most of the work is paying close attention to structural attacks and to the best of our knowledge, there is no effort on how to resist …

Disclosure Risk from Homogeneity Attack in Differentially …

WebThis is a serious type of attack, as it has legal consequences for data owners according to many laws and regulations worldwide. From the definition it also follows that an attacker … Web3 apr. 2024 · Many algorithms like k-anonymization, l-diversity and t-closeness have been proposed, but each of them has their own advantages and disadvantages. For example taking into account k-anonymization,... how many calories in a can of ravioli

B-Anonymization: Privacy beyond kAnonymization and l-Diversity

WebLocation Homogeneity Attack. Another advanced inference attack is called Map Matching Attack (MMA) [67], where the attacker can use external background knowledge about a … WebMachanavajjhala et al. [2] showed that an attacker can obtain the value of a sensitive attribute for a record if the diversity in that attribute is low (Homogeneity attack). In … Web23 mei 2024 · We propose a method that maximizes the distributional similarity of the synthetic data relative to the original data using a measure known as the pMSE, while guaranteeing epsilon-differential privacy. Additionally, we relax common DP assumptions concerning the distribution and boundedness of the original data. how many calories in a captain and coke