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.
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.
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 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.
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.
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 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.
This patent describes a user-centric solution for managing privacy settings more effectively. It introduces a "Personalized Privacy Charter" (PPC) that acts as a master control panel for users to set their privacy preferences. The PPC simplifies privacy management and establishes different degrees of data scarcity and protection. The patent outlines steps for onboarding users, mapping preferences to legal provisions, auditing social media privacy settings, fine-tuning the PPC, and confirming and deploying the PPC. The patent also covers a Corporate Privacy Charter (CPC) for companies to set privacy preferences.
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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