相关论文: Pretty Good Phone Privacy

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these…

Localization in mobile networks has been widely applied in many scenarios. However, an entity responsible for location estimation exposes both the target and anchors to potential location leakage at any time, creating serious security…

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy.…

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this…

As 5G networks become more mainstream, privacy has come to the forefront of end users. More scrutiny has been shown to previous generation cellular technologies such as 3G and 4G on how they handle sensitive metadata transmitted from an end…

With mobile phone penetration rates reaching 90%, Consumer Proprietary Network Information (CPNI) can offer extremely valuable information to different sectors, including policymakers. Indeed, as part of CPNI, Call Detail Records have been…

Cellular networks represent a critical infrastructure and their security is thus crucial. 5G - the latest generation of cellular networks - combines different technologies to increase capacity, reduce latency, and save energy. Due to its…

Mobile communication systems now constitute an essential part of life throughout the world. Fourth generation "Long Term Evolution" (LTE) mobile communication networks are being deployed. The LTE suite of specifications is considered to be…

Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at…

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy:…

While becoming more and more present in our every day lives, services that operate on users' locations or location trajectories suffer from general fear of misappropriation of the transmitted location data. Several works have investigated…

Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator…

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of…

Lengthy and legally phrased privacy policies impede users' understanding of how mobile applications collect and process personal data. Prior work proposed Contextual Privacy Policies (CPPs) for mobile apps to display shorter policy snippets…

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users,…

Ensuring user privacy remains critical in mobile networks, particularly with the rise of connected devices and denser 5G infrastructure. Privacy concerns have persisted across 2G, 3G, and 4G/LTE networks. Recognizing these concerns, the 3rd…

Nowadays privacy in the connected world is a big user's concern. The ubiquity of mobile devices permits billions of users browse the web at anytime, anywhere. Near Field Communication (NFC) appeared as a seamlessly and simply communication…

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as…

Geosocial networks are online social networks centered on the locations of subscribers and businesses. Providing input to targeted advertising, profiling social network users becomes an important source of revenue. Its natural reliance on…

Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the obfuscation of the sensitive information is done at the level of the individual records, and in general it is used to sanitize data that are…