A data processing method receives a set of time-series user data and also receives a privacy requirement of the time-series user data. Next, the time-series user data is transformed using the privacy requirement such that the transforming satisfies differential privacy.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data processing method, said method comprising: receiving a set of time-series user data; receiving a privacy requirement of said time-series user data; and transforming said time-series user data using said privacy requirement wherein said transforming satisfies differential privacy. 2. A method as recited in claim 1 wherein said set of time series user data is received at an electronic device, said method further comprising: performing said transforming on said electronic device; sending said transformed time-series data from said electronic device to a remote computer server, wherein the remote computer server is able to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data 3. A method as recited in claim 1 said method further comprising: receiving said set of time-series user data from an electronic device at a remote computer server; performing said transforming on said remote computer server; performing a machine learning analytics task on said transformed time-series user data in order to produce an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 4. A method as recited in claim 1 said method further comprising: receiving said set of time-series user data from an electronic device at a remote computer server; performing said transforming on said remote computer server; sending said transformed time-series user data to a second computer server, wherein the second computer server is able to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 5. A method as recited in claim 1 further comprising: before said transformation of said user data, compressing said user data using random projections. 6. A method as recited in claim 1 further comprising: compressing said user data and preserving a Euclidean distance between a first time series and a second time series of said user data, said compressing occurring before said transforming of said data. 7. A method as recited in claim 5 further comprising: compressing said user data less the higher said privacy requirement 8. A non-transitory processor-readable medium that includes a program that when executed by a processor performs a method comprising: receiving a set of time-series user data; receiving a privacy requirement of said time-series user data; and transforming said time-series user data using said privacy requirement wherein said transforming satisfies differential privacy. 9. A medium as recited in claim 8 wherein said set of time series user data is received at an electronic device, said method further comprising: performing said transforming on said electronic device; sending said transformed time-series data from said electronic device to a remote computer server, wherein the remote computer server is able to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data 10. A medium as recited in claim 8 said method further comprising: receiving said set of time-series user data from an electronic device at a remote computer server; performing said transforming on said remote computer server; performing a machine learning analytics task on said transformed time-series user data in order to produce an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 11. A medium as recited in claim 8, said method further comprising: receiving said set of time-series user data from an electronic device at a remote computer server; performing said transforming on said remote computer server; sending said transformed time-series user data to a second computer server, wherein the second computer server is able to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 12. A medium as recited in claim 8, said method further comprising: before said transformation of said user data, compressing said user data using random projections. 13. A medium as recited in claim 8, said method further comprising: compressing said user data and preserving a Euclidean distance between a first time series and a second time series of said user data, said compressing occurring before said transforming of said data. 14. A medium as recited in claim 12 further comprising: compressing said user data less the higher said privacy requirement 15. An apparatus for transforming data, said apparatus comprising: a processor; a memory; an input that receives a set of time-series user data and that receives a privacy requirement of said time-series user data, said time-series user data and said privacy requirement being stored in said memory, said processor of said apparatus being configured to transform said time-series user data using said privacy requirement wherein said transforming satisfies differential privacy. 16. An apparatus as recited in claim 15 wherein said set of time series user data is received at an electronic device, said processor of said apparatus being further configured to perform said transforming on said electronic device and to send said transformed time-series data from said electronic device to a remote computer server, wherein the remote computer server is configured to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data 17. An apparatus as recited in claim 15, said processor of said apparatus being further configured to receive said set of time-series user data from an electronic device at a remote computer server, to perform said transforming on said remote computer server, and to perform a machine learning analytics task on said transformed time-series user data in order to produce an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 18. An apparatus as recited in claim 15, said processor of said apparatus being further configured to receive said set of time-series user data from an electronic device at a remote computer server, to perform said transforming on said remote computer server, and to send said transformed time-series user data to a second computer server, wherein the second computer server is configured to perform a machine learning analytics task on said transformed time-series user data for producing an output, whereby said output is comparable to an output of said machine learning analytics task performed on said time-series user data. 19. An apparatus as recited in claim 15, said processor of said apparatus being further configured to compress said user data using random projections before said transformation of said user data. 20. An apparatus as recited in claim 15, said processor of said apparatus being further configured to compress said user data and preserve a Euclidean distance between a first time series and a second time series of said user data, said compressing occurring before said transforming of said data.