Recommendation Systems (RS) play an important role in our everyday life in this data-driven digital era by providing users with the convenience of navigating the plethora of available choices. An RS collects user behavioural data to provide them with valuable suggestions. The growing privacy concerns regarding private data collection have led to the use of Federated Learning (FL) to implement RS. However, many research works have exposed the privacy leakages in FL gradient sharing. The embedding gradients shared by FL users during the RS model training can be used to infer the items that users have interacted with. Existing defences, such as random noise injection or pseudo-interaction sampling to obfuscate the privacysensitive information reflected by the shared gradients. However, these techniques provide limited protection and often result in substantial degradation of recommendation performance, leading to an unfavourable privacy–utility trade-off. In this paper, we propose FedSIG (Federated Synthetic Interaction Generation), a defence mechanism that mitigates useritem interaction inference in federated recommendation systems by generating synthetic interaction data using generative models. The generated items are selectively used to replace or augment real user interactions, thereby obfuscating sensitive data while preserving user preference signals. To further enhance utility, we design an item selection module based on an attention mechanism to identify less contributive interactions for replacement. Extensive experiments conducted on five real-world datasets and two state-of-the-art recommendation models demonstrate that FedSIG achieves a significantly improved privacy–utility balance compared to existing approaches, effectively reducing inference success rates while maintaining competitive recommendation accuracy.

Decentralized machine learning, such as Federated Learning (FL), is widely adopted in many application domains. Especially in domains like recommendation systems, sharing gradients instead of private data has recently caught the research community’s attention. Personalized travel route recommendation utilizes users’ location data to recommend optimal travel routes. Location data is extremely privacy sensitive, presenting increased risks of exposing behavioral patterns and demographic attributes. FL for route recommendation can mitigate the sharing of location data. However, this article shows that an adversary can recover the user trajectories used to train the federated recommendation models with high proximity accuracy. To this effect, we propose a novel attack called DeepSneak, which uses shared gradients obtained from global model training in FL to reconstruct private user trajectories. We formulate the attack as a regression problem and train a generative model by minimizing the distance between gradients. We validate the success of DeepSneak on two real-world trajectory datasets. The results show that we can recover the location trajectories of users with reasonable spatial and semantic accuracy.

Personalized location recommendation allows users to enjoy a seamless travel experience by suggesting the optimal travel locations/routes based on user preferences. Most service providers collect users' location data centrally to develop accurate route recommendation applications. Federated learning (FL) can be used as an inherent privacy-preserving mechanism in these applications to prevent users from sharing private data. However, recent research shows that FL is still vulnerable to privacy leakages. Therefore, many FL-based recommendation systems use Local Differential Privacy (LDP) to defend against such attacks. In this paper, we propose the Visited Location Inference Attack (VLIA), a novel attack for federated location recommendation systems through the lens of Membership Inference Attack (MIA). Specifically, we focus on inferring user behaviour data (visited locations) even when the federated recommendation system is protected with LDP. We design and implement VLIA leveraging both embedding and proximity information of locations, making the inference more accurate. Our extensive experiments with two state-of-the-art personalized route recommendation (PRR) systems implemented in the FL setting and two real-world trajectory datasets showcase the effectiveness of the VLIA attack. Our results show that LDP cannot defend VLIA unless the recommendation performance is significantly compromised.

Although a massive amount of bandwidth is available at mm-waves, physics dictates the use of legacy frequencies in the sub 6-GHz range. This necessitates dynamic spectrum access in the face of exponentially growing spectral demands. However, disorganized spectrum sharing causes interference, leads to a chaotic situation, and loss of capacity. Moreover, it is difficult to ensure that the primary users are compensated for sharing their licensed bands. We propose a Blockchain-based platform to address these limitations. A digital token, called spectral token, is introduced to validate and track the use of a licensed frequency band while enforcing sequential access to spectrum by secondary users to avoid interference. The proposed platform enables both advertising and sensing based spectrum sharing under different leasing policies. Such sharing and leasing policies are coded into smart contracts, which digitally enforce the contractual clauses of the leasing agreement. When a deal is made, the smart contract automatically transfers the spectral token between primary and secondary users within the agreed time frame while paying the primary user in cryptocurrency. We developed a proof of concept solution using the Ethereum Blockchain to demonstrate the utility of the proposed platform and its throughput and latency characteristics.