公开(公告)号：US11501008B2

公开(公告)日：2022-11-15

申请号：US16938741

申请日：2020-07-24

Applicant: Apple Inc.

Inventor： Abhishek Bhowmick ,  Andrew H. Vyrros ,  Matthew R. Salesi ,  Umesh S. Vaishampayan

IPC: G06F21/60 ,  G06F17/16 ,  G06F17/14 ,  G06F16/26 ,  H04L9/32 ,  G06F21/62 ,  G06F17/18

Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.

公开(公告)号：US10726139B2

公开(公告)日：2020-07-28

申请号：US15721894

申请日：2017-09-30

Applicant: Apple Inc.

Inventor： Abhishek Bhowmick ,  Andrew H. Vyrros ,  Matthew R. Salesi ,  Umesh S. Vaishampayan

IPC: G06F21/60 ,  G06F17/16 ,  G06F17/14 ,  G06F16/26 ,  H04L9/32 ,  G06F21/62 ,  G06F17/18

Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.

公开(公告)号：US20180349620A1

公开(公告)日：2018-12-06

申请号：US15721894

申请日：2017-09-30

Applicant: Apple Inc.

Inventor： Abhishek Bhowmick ,  Andrew H. Vyrros ,  Matthew R. Salesi ,  Umesh S. Vaishampayan

IPC: G06F21/60 ,  G06F17/30 ,  G06F17/14 ,  G06F17/16

Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.