Private decayed sum estimation under continual observation
First Claim
1. A method for providing privacy protection, comprising:
- accessing dynamic data from a database;
selecting a decay sum model;
initiating an algorithm adapted to produce a polylogarithmic bounded error in the range of a sum function associated with the selected sum model and time step independent;
assembling the dynamic data in a dyadic tree structure;
adding a non-uniformity component to nodes of the dyadic tree structure;
constructing differentially private estimators for fixed steps of time; and
applying the differentially private estimators to a query to enhance privacy protection from potential adversaries.
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Abstract
Described herein is a method and system for providing privacy guarantees with an improved privacy-accuracy trade-off. Dynamic data can be accessed from a database. A sum model is selected from window sum, exponential decay sum, and polynomial decay sum. An algorithm is initiated that produces polylogarithmic bounded error in the range of a sum function associated with the selected sum model and independent of time steps. The data can be assembled in a dyadic tree structure. A non-linearity component can be added to nodes of the dyadic tree structure. For example, this can be a noise components or a weight applied to the update. This can be done, for example, to different nodes differently. Differential private estimators can be constructed for fixed steps of time. The differential private estimators can be applied to a query means or filtering system to enhance privacy protection from potential adversaries.
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Citations
20 Claims
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1. A method for providing privacy protection, comprising:
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accessing dynamic data from a database; selecting a decay sum model; initiating an algorithm adapted to produce a polylogarithmic bounded error in the range of a sum function associated with the selected sum model and time step independent; assembling the dynamic data in a dyadic tree structure; adding a non-uniformity component to nodes of the dyadic tree structure; constructing differentially private estimators for fixed steps of time; and applying the differentially private estimators to a query to enhance privacy protection from potential adversaries. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for providing privacy protection, comprising:
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a memory storing a query module, an analysis module, and an output module, the query module configured to access a database having continuously updated data; the analysis module configured to select a decay sum model; the analysis module configured to initiate an algorithm adapted to produce polylogarithmic bounded error in the range of a sum function associated with the selected sum model and time step independent; the analysis module configured to assemble the dynamic data in a dyadic tree structure; the analysis module configured to add a non-uniformity component to nodes of the dyadic tree structure; the analysis module configured to construct differentially private estimators for fixed steps of time; and the output module configured to apply the differentially private estimators to a query to enhance privacy protection from potential adversaries. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer readable medium bearing instructions for protecting
privacy of data, comprising: -
instructions for accessing dynamic data from a database; selecting a decay sum model; initiating an algorithm adapted to produce polylogarithmic bounded error in the range of a sum function associated with the selected sum model and time step independent; assembling the dynamic data in a dyadic tree structure; adding a non-uniformity component to nodes of the dyadic tree structure; constructing differentially private estimators for fixed steps of time; and applying the differentially private estimators to a query to enhance privacy protection from potential adversaries. - View Dependent Claims (18, 19, 20)
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Specification