- Carlsbad CA, US Peter J. Lepeska - Boston MA, US Douglas C. Larrick - Newton MA, US Devin R. Toth - Quincy MA, US
Assignee:
VIASAT, INC. - Carlsbad CA
International Classification:
G06F 21/62 G06F 16/957 H04L 29/08
Abstract:
Embodiments seek to protect privacy of potentially sensitive client resources in web transactions using crowd-disambiguation. Crowd-disambiguation machines can aggregate information about resources from multiple clients as resource fingerprints, and can use the fingerprints to provide crowd-sourced services in a privacy-protected manner For example, embodiments can communicate a resource fingerprint as a fully ambiguated resource instance (FARI) and a partially disambiguated resource instance (PDRI). When one (or few) clients communicates the resource fingerprint, the identity of the resource remains obfuscated from the crowd-disambiguation machine. As more clients communicate fingerprints for the same resource (e.g., identified by the matching FARIs), respective, differently generated PDRIs of those fingerprints enable the crowd-disambiguation machine to resolve further portions of the resource, ultimately permitting the resource to be revealed and considered non-private (e.g., for use in hint generation or other crowd-sourced services).
The present disclosure relates to prefetching dynamic URLs. For example, one disclosed method includes the steps of receiving bread-crumb information from a first client device, the bread-crumb information comprising a dynamic URL, a dynamically-generated value, and an indication of a method of generating the dynamically-generated value; determining a template for the dynamic URL based on the dynamically-generated value and the method of generating the dynamically-generated value; receiving a request for a hint for the URL; and in response to receiving the request for the hint from a second client device, transmitting the template for the dynamic URL to the second client device.
Machine-Driven Crowd-Disambiguation Of Data Resources
- CARLSBAD CA, US PETER J LEPESKA - BOSTON MA, US DOUGLAS C LARRICK - NEWTON MA, US DEVIN R TOTH - QUINCY MA, US
Assignee:
VIASAT, INC. - CARLSBAD CA
International Classification:
G06F 21/62 G06F 17/30
Abstract:
Embodiments use crowd disambiguation techniques to protect the privacy of potentially sensitive client resources in web transactions. Crowd disambiguation servers can aggregate information about resources, such as URLs, accessed by clients, in the form of resource fingerprints submitted by the clients. Said resource fmgerprints can be used to provide crowd-sourced services in a privacy-protected manner. For example, in some embodiments a fingerprint of a URL visited by a client can be communicated to the server as both a fully ambiguated resource instance (FARI) and a partially disambiguated resource instance (PDRI). When only one client, or a limited number of clients, has communicated a certain resource fmgerprint, the underlying identity of the resource, in this case the URL, remains obfuscated from the crowd disambiguation server, which lacks sufficient information to reconstruct it. As more clients communicate fmgerprints for the same resource (as identified, for example, by the FARIs), the corresponding PDRIs, which are different from client to client, enable the crowd disambiguation server to gradually reconstruct further portions of the resource, ultimately permitting the entire resource to be reconstructed. In that case, the resource is considered non-private, and can be further used e.g., in hint generation or other crowd-sourced services.
Embodiments seek to improve prefetch hinting using time-dependent, machine-generated hints. Some embodiments operate in context of client machines in communication hinting machines that can develop information about whether and how resources are used in network transactions over time by collecting “resource samples.” Each resource sample can identify rendering status information of a resource at a sample time. The time-based samples can be used to compute time-based probabilities for the resources, indicating, for example, the likelihood of a resource being used to render a web page at some subsequent time. Time-dependent hints can be generated as a function of the time-based probabilities, and the time-dependent hints can be used to improve prefetching by optimizing the hinting information with respect to a particular request time (e.g., the prefetching hints for rendering a web page can be generated in a manner that accounts for when the web page is being rendered).