In the era of pervasive online social networks (OSNs), the erosion of information privacy is occurring at an unprecedented rate. Empowering individuals with user-centric control over their private information is crucial to fostering public confidence in OSN services. Hence, the investigation into the personalized privacy configurations within the framework of differential privacy for OSNs, particularly for social relationships, is captivating. In this paper, we introduce a Collaborative Personalized Edge Differential Privacy model (CPEDP), ensuring personalized protection for sensitive social relationships while retaining the high utility of network features. Specifically, CPEDP allows each user to define a policy specification consisting of two complementary components: secret specifications at the edge level to identify sensitive relationships, and privacy specifications at the user level to determine personalized privacy parameters. These user-defined preferences are integrated through a collaborative privacy decision-making process that ensures consistent and interpretable privacy guarantees. Furthermore, we formalize the privacy primitive of CPEDP and develop a sampling-based mechanism to effectively implement the proposed model. Finally, comparative experiments on real-world datasets confirm that CPEDP achieves superior privacy-utility trade-offs, yielding more accurate estimates of key graph statistics through policy-driven personalization.

The proliferation of Internet of Things (IoT) devices has led to a surge in data generation within smart home environments. This data explosion has raised significant privacy concerns and highlighted a lack of user-friendly controls. Consequently, there is a pressing need for a robust privacy-enhancing mechanism tailored for smart homes, safeguarding sensitive data from a user-centric perspective. In this paper, we introduce the Fog-enhanced Personalized Differential Privacy (FEPDP) model, which utilizes the distributed nature of fog computing to improve data processing efficiency and security in smart homes. Specifically, the personalization, as a key feature of FEPDP, is manifested through an array of user-driven policy specifications, enabling home users to specify secret and privacy specifications for their personal data. These specifications not only enhance control over personal data but also align with the heterogeneous nature of smart home environments. Subsequently, aligned with fog-based smart home architecture, we propose two policy-driven partitioning mechanisms that utilize threshold partitioning based on dynamic programming to effectively implement FEPDP. Finally, comprehensive theoretical analysis and experimental validation across various statistical analysis tasks and datasets confirm that FEPDP achieves a superior privacy-utility trade-off for smart home data by leveraging non-sensitive data and fog-based partitioning.

With the prevalence of online social networks (OSNs), much personal information is collected and maintained by trusted service providers for third-party queries and analyses. Existing works regarding differentially private social network data publication overlook the fact that different users exhibit distinct privacy preferences or sensitivity inclinations. Neglecting these individual nuances may lead to privacy mechanisms that are overly conservative or inadequately protective. Furthermore, the injection of excessive noise into OSN data perceived by users as non-personal or less sensitive can incur additional privacy costs, resulting in lower service quality. This paper introduces a fine-grained, sensitivity-aware personalized edge differential privacy model (SPEDP) for OSNs. Specifically, SPEDP enables each OSN user to individually define the sensitivity level of their social connections, facilitating user-friendly personalized privacy settings. We design a privacy-aware mechanism that operates within a trusted service provider, capable of establishing privacy protection levels based on user-perceived sensitivity settings. Additionally, we propose a sensitivity-aware sampling mechanism to implement SPEDP. To further optimize the privacy mechanism, we explore a privacy threshold optimization strategy aimed at minimizing privacy budget waste. Finally, the personalized privacy protections and utility improvements achieved by the SPEDP mechanism are rigorously validated through theoretical analysis and comprehensive comparative experiments on benchmark datasets.

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user’s personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model’s feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation.

The growing prevalence of graphs representations in our society has led to a corresponding rise in the publishing of graphs by researchers and organizations. To protect the privacy, it is important to ensure that graphs including sensitive data are not disclosed. Since the weight of edges could be utilized to infer confidential information, the graph should be privately published to avoid ethical and legal issues. In this paper, we propose a novel method for privately publishing shortest paths while preserving the privacy of sensitive edge weights in graph. Specifically, we divide the edge weights into internal and external edges based on their edge betweenness centrality. Then, we give two different differentially private algorithms to perturb edge weights based on the distinction between internal and external edges, respectively. To reduce the error ratios between differentially private shortest paths and real shortest paths, we employ edge betweenness centrality to search for the shortest path, which is closest to the true one. Our experimental results show that our mechanisms can effectively reduce the error in the average shortest path distance by 1.1% for large graphs, while for the shortest path change rate, our mechanisms can reduce it by 8.3%.