Machine Learning techniques are widely used by online services (e.g. Google, Apple) in order to analyze and make predictions on user data. As many of the provided services are user-centric (e.g. personal photo collections, speech recognition, personal assistance), user data generated on personal devices is key to provide the service. In order to protect the data and the privacy of the user, federated learning techniques have been proposed where the data never leaves the user's device and "only" model updates are communicated back to the server. In our work, we propose a new threat model that is not concerned with learning about the content - but rather is concerned with the linkability of users during such decentralized learning scenarios. We show that model updates are characteristic for users and therefore lend themselves to linkability attacks. We show identification and matching of users across devices in closed and open world scenarios. In our experiments, we find our attacks to be highly effective, achieving 20x-175x chance-level performance. In order to mitigate the risks of linkability attacks, we study various strategies. As adding random noise does not offer convincing operation points, we propose strategies based on using calibrated domain-specific data; we find these strategies offers substantial protection against linkability threats with little effect to utility.
Linkability Attacks Linkability attacks involve an adversary attributing one or more disconnected entities (e.g., social media profiles, text) to a single identity. A number of works have studied linkability threats on social networks, such as cross-site identity tracking to match profiles of users based. This is typically done using various user cues -- profile attributes @cite_70 @cite_73 @cite_76 , geo-location @cite_42 , social graph structure @cite_9 @cite_26 @cite_31 or content @cite_30 @cite_28 . Some works have addressed linking written texts such as reviews or articles using the stylometric features @cite_71 @cite_22 . Recently, @cite_37 studied the temporal linkability of microRNA expression profiles. In this work, to the best of our knowledge, we are the first to demonstrate a new linkability threat based on incremental parameter updates of users' ML models.
@cite_30 @cite_37 @cite_26 @cite_22 @cite_73 @cite_70 @cite_28 @cite_9 @cite_42 @cite_71 @cite_31 @cite_76
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Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due to the diversity and complexity of graph data, on which an adversary can use many types of background knowledge to conduct an attack. One popular type of attacks as studied by pioneer work [2] is the use of embedding subgraphs. We follow this line of work and identify two realistic targets of attacks, namely, NodeInfo and LinkInfo. Our investigations show that k-isomorphism, or anonymization by forming k pairwise isomorphic subgraphs, is both sufficient and necessary for the protection. The problem is shown to be NP-hard. We devise a number of techniques to enhance the anonymization efficiency while retaining the data utility. A compound vertex ID mechanism is also introduced for privacy preservation over multiple data releases. The satisfactory performance on a number of real datasets, including HEP-Th, EUemail and LiveJournal, illustrates that the high symmetry of social networks is very helpful in mitigating the difficulty of the problem. The existence of user profiles belonging to a single user across different social networking sites poses several challenges to the research community. The main technical issue in that context is to detect the same user profiles across several different social networks by leveraging a set of mechanisms that identify the similarity among the user profiles. This problem is commonly referred to as entity matching or identity linkage on social networks. In this review, we describe and compare the 27 most important (to the best of our knowledge) research papers in this area. The main contributions of this article are to provide a systematic and integrated review of papers in this area, to provide comparative points that simplify the understanding of such systems, and finally to discuss future research avenues. We apply adjacency matrix clustering to network attack graphs for attack correlation, prediction, and hypothesizing. We self-multiply the clustered adjacency matrices to show attacker reachability across the network for a given number of attack steps, culminating in transitive closure for attack prediction over all possible number of steps. This reachability analysis provides a concise summary of the impact of network configuration changes on the attack graph. Using our framework, we also place intrusion alarms in the context of vulnerability-based attack graphs, so that false alarms become apparent and missed detections can be inferred. We introduce a graphical technique that shows multiple-step attacks by matching rows and columns of the clustered adjacency matrix. This allows attack impact responses to be identified and prioritized according to the number of attack steps to victim machines, and allows attack origins to be determined. Our techniques have quadratic complexity in the size of the attack graph This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage "relational" evidences extracted from the Web corpus. In particular, as such an example, weadopt Web document co-occurrences, which can be interpreted as an "implicit" counterpart of Twitter follower relationships. Using both textual and relational features, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate to outperform the baseline approach. We evaluate our proposed system using real-life internetscale entity-relationship and social network graphs.