Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications. However, its remarkable success has been accompanied by various attacks. Recently, the model hijacking attack has shown that ML models can be hijacked to execute tasks different from their original tasks, which increases both accountability and parasitic computational risks. Nevertheless, thus far, this attack has only focused on centralized learning. In this work, we broaden the scope of this attack to the federated learning domain, where multiple clients collaboratively train a global model without sharing their data. Specifically, we present the first-of-its-kind hijacking attack against the global model in federated learning, namely HijackFL. The adversary aims to force the global model to perform a different task (called hijacking task) from its original task without the server or benign client noticing. To accomplish this, unlike existing methods that use data poisoning to modify the target model’s parameters, HijackFL searches for pixel-level perturbations based on their local model (without modifications) to align hijacking samples with the original ones in the feature space. When performing the hijacking task, the adversary applies these perturbations to the hijacking samples, compelling the global model to identify them as original ones and predict them accordingly. Extensive experiments demonstrate HijackFL significantly outperforms baselines, e.g., 92.75% vs. 10%. We further investigate the factors that affect its performance and discuss possible defenses to mitigate its impact.

Many organizations own data but have limited machine learning expertise (data owners). On the other hand, organizations that have expertise need data from diverse sources to train truly generalizable models (model owners). With the advancement of machine learning (ML) and its growing awareness, the data owners would like to pool their data and collaborate with model owners, such that both entities can benefit from the obtained models. In such a collaboration, the data owners want to protect the privacy of its training data, while the model owners desire the confidentiality of the model and the training method that may contain intellectual properties. Existing private ML solutions, such as federated learning and split learning, cannot simultaneously meet the privacy requirements of both data and model owners.

Communication networks need to be both adaptive and scalable. The last few years have seen an explosive growth of software-defined networking (SDN) and network function virtualization (NFV) to address this need. Both technologies help enable networking software to be decoupled from the hardware so that software functionality is no longer constrained by the underlying hardware and can evolve independently. Both SDN and NFV aim to advance a software-based approach to networking, where networking functionality is implemented in software modules and executed on a suitable cloud computing platform. Achieving this goal requires the virtualization paradigm used in these services that play an important role in the transition to software-based networks. Consequently, the corresponding computing platforms accompanying the virtualization technologies need to provide the required agility, robustness, and scalability for the services executed. Serverless computing has recently emerged as a new paradigm in virtualization and has already significantly changed the economics of offloading computations to the cloud. It is considered as a low-latency, resource-efficient, and rapidly deployable alternative to traditional virtualization approaches, such as those based on virtual machines and containers. Serverless computing provides scalability and cost reduction, without requiring any additional configuration overhead on the part of the developer. In this paper, we explore and survey how serverless computing technology can help building adaptive and scalable networks and show the potential pitfalls of doing so.

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.

Mobile social apps provide sharing and networking opportunities based on a user's location, activity, and set of nearby users. A platform for these apps must meet a wide range of communication needs while ensuring users' control over their privacy. In this paper, we introduce EnCore, a mobile platform that builds on secure encounters between pairs of devices as a foundation for privacy-preserving communication. An encounter occurs whenever two devices are within Bluetooth radio range of each other, and generates a unique encounter ID and associated shared key. EnCore detects nearby users and resources, bootstraps named communication abstractions called events for groups of proximal users, and enables communication and sharing among event participants, while relying on existing network, storage and online social network services. At the same time, EnCore puts users in control of their privacy and the confidentiality of the information they share. Using an Android implementation of EnCore and an app for event-based communication and sharing, we evaluate EnCore's utility using a live testbed deployment with 35 users.

Content distribution systems have traditionally adopted one of two architectures: infrastructure-based content delivery networks (CDNs), in which clients download content from dedicated, centrally managed servers, and peer-to-peer CDNs, in which clients download content from each other. The advantages and disadvantages of each architecture have been studied in great detail. Recently, hybrid, or 'peer-assisted', CDNs have emerged, which combine elements from both architectures. The properties of such systems, however, are not as well understood.