The Privacy-Inclusive Data Access (PIDA) agent enables conditional, licensed access to personal and enterprise data for the emerging $6 trillion global data marketplace
Supported by 10 patents, Cambrian’s PIDA-BouncerBot concept design balances data privacy and conditional access for individuals and enterprises alike. The comprehensive methodology enables trusted data sharing with fine-grained access control based on individual dynamically adjusting privacy terms, watermarking, decoying, alternative DSP recommendations, data-footprint scoring and trading.
Challenge 1
Dozens of trackers follow your digital movements day and night, capturing thousands of data points. Indiscriminate usage and triangulation of that data undermines a privacy-assured, transparent, and value-based exchange, creating both public distrust and enterprise “data debt.”
Challenge 2
Personal data sets have different relevance and value for the individual and for enterprises. The PIDA generates scores bi-directionally – for data sellers (individuals) and data buyers (enterprises) – so both sides have access to a trusted knowledge base for transparent market transactions.
Identify and use valuable data in your enterprise value chain rather than acquiring useless data sets.
Protect against triangulation’s rapidly growing share of $5.2 trillion in cybersecurity damages by accessing data selectively.
Maintain compliance and minimize regulatory "data debt" as laws and regulations multiply.
Commit to the ethical use of data, regain the trust of 60% of people and mitigate backlash against tech.
This patent describes a method for protecting internet users' data privacy by analyzing and categorizing the clauses in online privacy policies. The process involves creating a dataset of privacy policies, tokenizing and preprocessing the data, clustering and topic modeling, classification modeling using machine learning algorithms, and applying the models to test data. The goal is to identify privacy-violating clauses and calculate a weighted average privacy score for each policy.
The present invention generally relates to the field(s) of defining privacy policies for data originating from or belonging to individuals and companies/employees. More specifically, embodiments of the present invention pertain to computer-implemented systems and methods for helping users and companies protect their data in a manner that reflects their personal preferences for different data types, contexts, etc using a variable, customizable data policy.
A configurable, customizable privacy protecting software agent operates on behalf of a user to control the dissemination and use of the user's personal data. The software agent is guided by a personal/corporate privacy charter specified by the user (or an enterprise manager), which charter is adapted dynamically based on user and site conditions. The agent engages with digital service provider (DSP) sites/apps on users' behalf, and notifies them of privacy incompatibilities, issues, etc. associated with the DSPs, along with recommended alternatives if available or possible. The agent can also tag user data and monitor unauthorized uses to report on DSP compliance with user specified policies.
This patent describes a method for protecting internet users' data privacy by analyzing and categorizing the legal clauses in online privacy policy agreements. The method involves utilizing computers, software, and data science algorithms to parse and understand privacy policies. The process includes steps such as dataset creation, tokenization, preprocessing, vectorization, clustering, classification, and applying fitted models for evaluation. The goal is to provide users with a weighted average privacy score (WAPS) for each privacy policy, allowing them to make informed decisions about their privacy. The method aims to address the cumbersome and user-unfriendly nature of existing privacy policy agreements.
The patent focuses on embedding tracking codes or identifying information into user data using steganography and watermarking techniques. It proposes modifying data without affecting its quality or usability to make the changes undetectable. The patent also discusses the storage of metadata associated with the embedded watermark, including user ID, creation/transmission dates, and permissions. Distributed ledger technologies like blockchain are preferred for secure and immutable metadata storage.
An automated system tracks digital service providers (DSP) data management agreements, DSP behavior, and user behavior, individually and in aggregate, to determine recommended alternatives for content/service sites/providers than those used by a user. The alternatives are selected based on their scoring and congruency or compliance with a user's target privacy data treatment parameters.
The patent focuses on masking a user's online data footprint by deploying decoys and false requests to trackers and data aggregators. The privacy agent generates fake requests and responses to obscure the user's true information. Various use cases are described, including geo-location decoys, media consumption, online search, and financial transactions. The goal is to negotiate fair data agreements with service providers and give users control over their data. The approach offers immediacy, user-controlled enforcement of agreements, and advantages over other digital rights management methods.
The patent describes a system and method for creating an efficient and equitable data marketplace. It addresses limitations in existing data trading models by introducing a dynamic pricing mechanism and user-centric data and privacy management tools. The system allows data creators to set their own privacy terms, negotiate data usage, and control the flow of their data. It also emphasizes the importance of data ownership and proposes legal protection and prevention of data sharing as means to establish ownership. The system includes transaction infrastructure modules for duplicating and bundling data, tagging data with identifiers, issuing data usage licenses, and matching supply and demand.
Olaf Groth, Tobias Straube, and Dan Zehr discuss the development of an equitable data marketplace. It addresses the balance of privacy and compensation for data creators, emphasizing the need for transparent and fair data trading mechanisms.
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