Abstract Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users’ training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is simple yet effective, capable of leaking potential sensitive information represented by labels, and scales well to arbitrary batch sizes and multiple classes. We mathematically and empirically demonstrate the validity of the attack under different settings. Moreover, empirical results show that LLG successfully extracts labels with high accuracy at the early stages of model training. We also discuss different defense mechanisms against such leakage. Our findings suggest that gradient compression is a practical technique to mitigate the attack.

Decentralized online social networks enhance users’ privacy by empowering them to control their data. However, these networks mostly lack for practical solutions for building recommender systems in a privacy-preserving manner that help to improve the network’s services. Association rule mining is one of the basic building blocks for many recommender systems. In this paper, we propose an efficient approach enabling rule mining on distributed data. We leverage the Metropolis-Hasting random walk sampling and distributed FP-Growth mining algorithm to maintain the users’ privacy. We evaluate our approach on three real-world datasets. Results reveal that the approach achieves high average precision scores () for as low as 1% sample size in well-connected social networks with remarkable reduction in communication and computational costs.

Empowered by the high connectivity of manifold devices in today's world, distributed machine learning enables multiple, distributed users to build a joint model by sharing their gradients over a network. In this paper, we highlight the privacy risk of sharing gradients by proposing LLG, an algorithm to disclose the labels of the users' training data from their shared gradients. We conduct an empirical analysis on two datasets to demonstrate the validity of our algorithm. Results show that our approach effectively extracts the labels with high accuracy in different scenarios.

Most research in the field of network intrusion detection heavily relies on datasets. Datasets in this field, however, are scarce and difficult to reproduce. To compare, evaluate, and test related work, researchers usually need the same datasets or at least datasets with similar characteristics as the ones used in related work. In this work, we present concepts and the Intrusion Detection Dataset Toolkit (ID2T) to alleviate the problem of reproducing datasets with desired characteristics to enable an accurate replication of scientific results. Intrusion Detection Dataset Toolkit (ID2T) facilitates the creation of labeled datasets by injecting synthetic attacks into background traffic. The injected synthetic attacks created by ID2T blend with the background traffic by mimicking the background traffic’s properties. This article has three core contributions. First, we present a comprehensive survey on intrusion detection datasets. In the survey, we propose a classification to group the negative qualities found in the datasets. Second, the architecture of ID2T is revised, improved, and expanded in comparison to previous work. The architectural changes enable ID2T to inject recent and advanced attacks, such as the EternalBlue exploit or a peer-to-peer botnet. ID2T’s functionality provides a set of tests, known as TIDED, that helps identify potential defects in the background traffic into which attacks are injected. Third, we illustrate how ID2T is used in different use-case scenarios to replicate scientific results with the help of reproducible datasets. ID2T is open source software and is made available to the community to expand its arsenal of attacks and capabilities.

Today's commercial online social networks (COSNs) are under continuous debate for their lack of proper data and privacy protection; meanwhile, privacy-preserving online social networks (PPOSNs) struggle to attract a critical number of users. In this paper, we propose a hybrid solution that combines the best of both worlds in a seamless experience. Our hybrid online social network (HOSN) concept enables users to gradually transition parts of their online social experience to a more privacy- preserving network, while also considering the needs of the commercial providers. A reference implementation for Twitter with a detailed technical discussion proves the viability of this concept.

Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain sensitive personal information (e.g., political views). This potentially even inhibits the user from providing information in the first place. Current distributed privacy-preserving association rules mining (PPARM) approaches use cryptographic primitives that come with high computational and communication costs, rendering PPARM unsuitable for large-scale applications such as social networks. We propose improvements in the efficiency and privacy of PPARM approaches by minimizing the required data. We propose and compare sampling strategies to sample the data based on social graphs in a privacy-preserving manner. The results on real-world datasets show that our sampling-based approach can achieve a high average precision score with as low as 50% sampling rate and, therefore, with a 50% reduction of communication cost.