Abstract:
Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
Abstract:
One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
Abstract:
Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
Abstract:
Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).
Abstract:
Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
Abstract:
Systems and methods are disclosed for generating term frequencies of known terms based on crowdsourced differentially private sketches of the known terms. An asset catalog can be updated with new frequency counts for known terms based on the crowdsourced differentially private sketches. Known terms can have a classification. A client device can maintain a privacy budget for each classification of known terms. Classifications can include emojis, deep links, locations, finance terms, and health terms, etc. A privacy budget ensures that a client does not transmit too much information to a term frequency server, thereby compromising the privacy of the client device.
Abstract:
Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
Abstract:
In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. In some implementations, before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.
Abstract:
Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.
Abstract:
Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.