Federated learning aims to complete model training without private data sharing, but many privacy risks remain. Recent studies have shown that federated learning is vulnerable to membership inference attacks. The weight as an important parameter in neural networks has been proven effective for membership inference attacks, but it leads to significant overhead. Facing this issue, in this paper, we propose a bias-based method for efficient membership inference attacks against federated learning. Different from the weight that determines the direction of the decision surface, the bias also plays an important role in determining the distance to move along the direction. Moreover, the number of bias is way less than the weight. We consider two types of attacks: local attack and global attack, corresponding to two possible types of insiders: participant and central aggregator. For the local attack, we design a neural network-based inference, which fully learns the vertical bias changes of the member data and non-member data. For the global attack, we design a difference comparison-based inference to determine the data source. Extensive experimental results on four public datasets show that the proposed method achieves state-of-the-art inference accuracy. Moreover, experiments prove the effectiveness of the proposed method to resist some commonly used defenses.

Named entity extractors can be used to enrich both text and Web documents with semantic annotations. While originally focused on a few standard entity types, the ecosystem of annotators is becoming increasingly diverse, with recognition capabilities ranging from generic to specialised entity types. Both the overlap and the diversity in annotator vocabularies motivate the need for managing and integrating semantic annotations: allowing users to see the results of multiple annotations and to merge them into a unified solution. We demonstrate ROSEANN, a system for the management of semantic annotations. ROSEANN provides users with a unified view over the opinion of multiple independent annotators both on text and Web documents. It allows users to understand and reconcile conflicts between annotations via ontology-aware aggregation. ROSEANN incorporates both supervised aggregation, appropriate when representative training data is available, and an unsupervised method based on the notion of weighted-repair.

A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications to benefit from the much richer vocabulary available in an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement. The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontology-unaware supervised approaches, and to individual annotators.

An increasing number of systems provide the ability to semantically annotate documents. OpenCalais [4], Evri API [2], Zemanta [6], and Alchemy API [1] are web-hosted systems that return annotated documents, i. e. documents with annotations that are overlayed on the document structure. Many of the annotations can be linked to standard ontologies, such as DBpedia and YAGO. These annotations give insight as to the meaning of documents in a variety of ways, identifying entities and relationships inside them, classifying them according to topic or theme, and giving the attitude or sentiment of a document or document fragment. In order for users (or applications) to make use of these annotations with a means to access and manipulate documents that contain them, we provide a query language for doing this and demonstrate its utility on a demo system built on top of diverse semantic annotators and external ontologies. We explain how integrating semantic annotations and utilizing external knowledge helps in increasing the quality of query answers over annotated documents by both filtering out irrelevant answers and obtaining extra answers that are not explicitly available in the annotated documents.