Responsive privacy-preserving system for detecting email threats
Abstract:
Embodiments of the present disclosure provide centralized and coordinate learning techniques for identifying malicious e-mails while maintaining privacy of the analyzed e-mails of different organizations. One or more models may be generated and configured to construct feature sets that may be used to characterize e-mails as malicious or safe. Feedback associated with one or more models trained by a first organization (and other organizations) may be shared with a modelling device to modify parameters of the one or more models, where the modified parameters are configured to improve identification of malicious e-mail threats. The feedback provided by the first organization may not include e-mails received by the first organization, thereby enabling the privacy of the e-mails received by the first organization to be maintained in an confidential manner even though the updated parameters may be shared with a second organization.
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