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Secure data interchange

a data exchange and data technology, applied in the field of secure data exchange, can solve the problems of user's desire for controlled personalization, large corpus of data extrapolation, and inability to be desirable, and achieve the effect of avoiding loss of privacy, facilitating bilateral exchange of profiles/preferences, and facilitating the exchange of information

Inactive Publication Date: 2009-10-08
STRIPE INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0026]The system of Secure Data Interchange (SDI) provides a trusted server containing a large database of information that is owned by its providers. Each data record has an associated price rule, that controls access to data. The pricing model allows a data owner to specify a price for different types and amounts of information access, and whether the identity of the information owner is required, and the system of SDI computes a composite price for a query based on aggregated prices for a query over a number of different data owners, with an internal market that favors low priced data. The pricing model allows discounts based on certificates of a requesting agent, and as a special case implements the standard capability-based access control systems, where information is provided to users with appropriate permissions (i.e. with zero and infinite prices). In addition, the system of Secure Data Interchange allows data to be submitted with a level of random perturbation (noise), to provide added privacy protection, or alternatively allow an agent to specify in conditions under which additional noise should be added to data. A query is priced before execution, to allow an agent to decide whether or not to execute a query, and select between alternative types of queries. Binding price quotes are provided to querying agents, and queries can be scaled to meet a budget.
[0039](e) We also suggest a client-side SDI proxy that can collect information about a user, for example within an Internet browsing application, and periodically push the collected information to the SDI data warehouse in a controlled way. The client-side SDI proxy can also be responsible for certain data certification functions, and can manage a user's interactions with other agents to protect its privacy in non-SDI mediated transactions.As an application to B2C e-commerce, the system of SDI allows client-side personalization instead of provider-side personalization. Instead of passing profile information to a provider and receiving personalized information in return, providers can provide personalization methods that are used interactively with local profile information about consumers to target products and services without receiving explicit information about a user's profile. In a simple form, the vendor provides complete information about its services, and a method to display them to the user based on his / her local profile. When describing the application of SDI to electronic commerce we also describe methods to implement necessary ancillary systems that are essential to supporting full e-commerce functionality within an identity-protected system, such as systems for pseudonymous payments and physical mailing of products.

Problems solved by technology

The problem is that a user wants controlled personalization, in the sense that it might not be desirable for information about every on-line transaction that a user performs, every on-line document that a user reads, and every web page that a user visits, and demographic information, to be available to every business that the user interacts with, in the virtual and physical world.
The problem—as before, is to acquire and leverage information about the preferences and interests of a user, within a system that protects user privacy (i.e. controls the collection and exchange of information about users, and controls the use that is made of that information).
A further problem is to extrapolate information from a large corpus of data about an individual user.
Consumer B meets the criteria, but is only listed for business A if A also meets criteria specified by B, for example if A will provide information about new products and services that are interesting to B. In an application to the profiling of users on-line, the problem is that users want to receive the benefits of targeted products and advertisements, but want to avoid the abuse of profile information and control vendors' access to that information.
The problem with this exchange of information (that can include swaps, sells, and rental access) is that businesses need to (a) protect the privacy of their customers; (b) prevent information release to competitors, either directly or through third-parties.
The problem is to provide information that enables matches, without allowing bad matches and abuse of information—i.e. within an environment of secure data interchange.
The problem is to manage certificates within a system where users can maintain multiple identities, and to protect the release of certificates without suitable provisions for terms-of-use and criteria for request.

Method used

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Examples

Experimental program
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case 1

[0628] All Customers Use a Single Pseudonym, and Appear in all Databases Considered.[0629]This is the simplest situation to handle. Since all customers appear in all the databases, the customer vectors' fields are essentially scattered across several locations, but can be easily reconstructed. For each customer, we define a new data vector that concatenates that customer's representation from across the different databases.

[0630]Hence, if we are considering databases A, B, . . . , Z, and customer i appears in each one, we define a new vector ci=(cAi, cBi, . . . , cZi), where cAi is customer i's vector in database A. We then proceed as usual, making inferences with these augmented customer vectors.

case 2

[0631] Most Customers Use a Unique Pseudonym, and Frequently Appear in Different Databases.[0632]In this situation, although we see some connections between the databases, many pseudonyms appear in only a single location. Using Bayesian techniques, however, we can still make predictions for customer vectors across databases.[0633]Suppose we have a set of databases, A, B, . . . , Z. Taking each database in turn, we cluster it using all available data. Thus, using every record in database A, we group A's customers into clusters A1, A2, . . . , An. Taking database B, we create clusters using all of B's information, creating customer clusters B1, B2, . . . , Bm, and so forth.[0634]Now, scan both databases for common pseudonyms (representing those customers who have interacted with both vendors under the same pseudonym) and create count variables wij to represent the number of pseudonyms that appear jointly in Ai and Bj.[0635]We can now produce the probability that a pseudonym appearing ...

case 3

[0640] All Customers Use Several Pseudonyms, and None Appear in Different Databases[0641]In this situation, there are no common customer codes that can be used to create links across the databases. However, the mere fact that several databases have been brought together for analysis should imply that there are semantic commonalties in the data.[0642]Although each database contains different fields, it may be the case that those fields deal with related subjects. A human expert, knowledgeable in the content of the databases, the subtleties of the domain, and the overall goal of the analysis (e.g. the creation of recommendations), will be in a position to create a “common-information profile” that spans the databases. In essence, the common-information profile defines a format that allows vectors from different databases to share a common coordinate space.[0643]The idea is this: the expert designs a high-level vector format that embodies the content deemed important for the project go...

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PUM

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Abstract

A secure data interchange system enables information about bilateral and multilateral interactions between multiple persistent parties to be exchanged and leveraged within an environment that uses a combination of techniques to control access to information, release of information, and matching of information back to parties. Access to data records can be controlled using an associated price rule. A data owner can specify a price for different types and amounts of information access.

Description

RELATED APPLICATIONS[0001]This application is a continuation of and claims priority under 35 U.S.C. §120 to U.S. application Ser. No. 09 / 699,098 entitled “Secure Data Interchange,” filed on Oct. 27, 2000, which claims the benefit of U.S. Provisional Application No. 60 / 161,640, filed Oct. 29, 1999, titled Secure Data Interchange, and Provisional Application No. 60 / 206,538, filed May 23, 2000, titled Secure Data Interchange, all of which are incorporated herein by reference in their entirety.BACKGROUND OF THE INVENTION[0002]1. Field of the Invention[0003]The Secure Data Interchange invention describes a system to allow a privacy-protected market for data exchange between multiple self-interested parties. The system presents a general infrastructure for the exchange of information within a safe privacy-protected environment, between multiple self-interested parties. We propose a central data warehouse that maintains data submitted by different users, and executes queries and programs o...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F17/00G06Q10/00
CPCG06Q10/10H04L63/20G06Q30/0603G06Q30/02G06F16/337Y10S707/99932Y10S707/99939
Inventor HERZ, FREDERICK S. M.LABYS, WALTER PAULPARKES, DAVID C.KANNAN, SAMPATHEISNER, JASON M.
Owner STRIPE INC
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