Abstract: Privacy-preserving federated learning apparatuses, systems, computer program products, and methods are provided that generate an updated global model based on a set of client models while maintaining privacy regarding the data values embodying each client model and the updated global model. In this regard, masked client models are utilized, which cryptographically obfuscate data values embodying the client model while still enabling combination, or “aggregation,” of the masked client models to generate a masked updated global model. The masked updated global model similarly includes obfuscated data values embodying the updated global model, but may be unmasked to reveal the true values of the updated global model for use. Some embodiments utilize specific steps for communication between environments, systems, devices, and/or the like, to ensure the masked models can only be unmasked by intended entities.

Abstract: Embodiments of the present disclosure provide for privacy-preserving personalized search, which enables accurate search personalization without exposing user data to third-party entities in a manner that may be illegal due to regional privacy restrictions and/or undesirable for purposes of data privacy protection. Such personalized search is provided via a privacy-preserving personalized search model that embodies or utilizes at least one model trained in a privacy-preserving manner, for example via privacy-preserving federated learning. Contrary to conventional systems, embodiments thus remain highly accurate while simultaneously remaining fully private. Additionally, embodiments of the present disclosure provide for privacy-preserving personalized search training to enable training of device(s) for privacy-preserving personalized search in an efficient manner and user-friendly manner utilizing search preference training interface(s).

Abstract: Embodiments of the present disclosure provide for privacy-preserving personalized search, which enables accurate search personalization without exposing user data to third-party entities in a manner that may be illegal due to regional privacy restrictions and/or undesirable for purposes of data privacy protection. Such personalized search is provided via a privacy-preserving personalized search model that embodies or utilizes at least one model trained in a privacy-preserving manner, for example via privacy-preserving federated learning. Contrary to conventional systems, embodiments thus remain highly accurate while simultaneously remaining fully private. Additionally, embodiments of the present disclosure provide for privacy-preserving personalized search training to enable training of device(s) for privacy-preserving personalized search in an efficient manner and user-friendly manner utilizing search preference training interface(s).

Abstract: Privacy-preserving federated learning apparatuses, systems, computer program products, and methods are provided that generate an updated global model based on a set of client models while maintaining privacy regarding the data values embodying each client model and the updated global model. In this regard, masked client models are utilized, which cryptographically obfuscate data values embodying the client model while still enabling combination, or “aggregation,” of the masked client models to generate a masked updated global model. The masked updated global model similarly includes obfuscated data values embodying the updated global model, but may be unmasked to reveal the true values of the updated global model for use. Some embodiments utilize specific steps for communication between environments, systems, devices, and/or the like, to ensure the masked models can only be unmasked by intended entities.