Navigate out of the labyrinth of complex and opaque privacy management to a world
where proactive data security and autonomy are not only achievable, but standard.
Problem for employees and consumers
In today's digital landscape, consumers face growing concerns over the lack of transparency, privacy, and control of their personal data. Their identities are at stake since every online move triangulates their life patterns. With worries escalating year after year, it's time to regain control and uncover the true value of personal data.
Problem for entreprises
Enterprises face a multitude of challenges, from protecting against data triangulation and preemption to designing trust and privacy into their offerings. Discover how our solution equips businesses to navigate this privacy battlefield, positioning them as responsible leaders, mitigating non-compliance risks, and renewing trust with consumers in an era of mass manipulation.
Unlock the potential of privacy with the BouncerBot, a product concept design engineered to safeguard your organization's digital footprint. Stay in control and navigate the online landscape with confidence.
AI-powered agent that helps users control their privacy
Designs a personal privacy charter for each user
Compares privacy agreements of digital service providers
Makes recommendations about trackers and websites
Tags user data to ensure compliance with the personal privacy charter
Gives users control over their data and negotiating power.
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.
People fuel the digital economy with streams of data but have no power to negotiate compensation for it. Olaf Groth and Tobias Straube with Dan Zehr propose a data market place that would make the digital economy more equitable.
Learn More
.png){kind=logo}
