Hi HN!I’ve built a mobile SDK with new user targeting architecture that improves user privacy, while also giving developers more precision and criteria to target users. I know that sounds super unintuitive, but by inverting standard user targeting architecture becomes possible. The SDK is live on Github, and we have full docs to try it out (links below).Here’s the high level idea of how it works (more detail in in blog post linked below):Zero data collection: the data flow is unidirectional from server to client. The client never needs to send information to the server for targetingPowerful on-device logic engine: you can write targeting logic with conditional strings using powerful but familiar syntax. It supports logical operators, functions, arithmetic, set operations, dates, random number generation, database queries, and more! This runs completely locally on each user’s device.Rich build-in targeting properties: 100 properties you can query, covering device information, user context, sensors, location, permissions, connectivity, peripherals, locale, app info, and much more.Local event database: each client builds a rich database of user engagement history (app launches, session times, terminations, and user actions, custom events, etc). You can query this and target users, without streaming interaction data to any server.Local database for property history: allows you to see if the current state is exceptional or the norm for this user.Logic isn’t hardcoded: you can still update your logic over the air anytime, without app updates. You simply push new logic to clients instead of updating server-side logic.Since everything is local and data never leaves device, we can offer more precise targeting criteria, without the additional scaling complexity, privacy concerns, costs, or legal concerns that come with server-side data collection of contextual data. We can do all this without IDFA or device fingerprinting.SDK docs: https://docs.criticalmoments.io/quick-startMore details on the architecture: https://criticalmoments.io/blog/how_to_target_users_without_...Github: https://github.com/CriticalMoments/CriticalMoments

Hi everyone — I wanted to share what we’ve been working on for the past few months. Monoid is a self-hosted, open-source first platform for automating data privacy requirements.A (very simplified) primer for people who aren’t as familiar with data privacy law: one common stipulation in a number of data privacy regulations (e.g. GDPR, CPRA, etc.) is the requirement for businesses to create a map of user data across places where the data is stored & fulfill requests (Data Subject Requests, or DSR’s) for export and/or deletion of this data.Lots of proprietary solutions exist to help businesses meet these guidelines (OneTrust, DataGrail, etc.) by integrating with the data silos that businesses use to aid them in mapping personal data and fulfilling the user privacy requests.I think that an open-source offering contributes to this category meaningfully, though, for a few reasons:1. It seems antithetical to not give companies the option to host software that integrates with sensitive data silos (especially with the intent of keeping it private) locally.2. The number of potential data silos means that virtually all existing solutions have coverage gaps for any given customer. We thought that Airbyte’s approach to solving this for ETL (by offering an easy way for people to build & contribute their own connectors) could be a smart way to solve this problem, so we used that inspiration to design Monoid. At Monoid’s core are open-source privacy connectors with DB’s, warehouses, SaaS tools, and more. These connectors let organizations automate user data export/deletion (for DSARs) and scan for/map PII.3. Open-source lowers the activation energy required for companies to comply with privacy law (two-line deploy)4. This isn’t a virtue of the open-source approach per-se, but we designed Monoid with full automation in mind; mapping, scanning, and request fulfillment are all automated because the privacy connectors are made to read/write/forward privacy requests to the data silos. Most software solutions in this space are missing this component, or have it only nominally.You can play around with a read-only demo account at https://demo.monoid.co, and sign up for our cloud offering at https://app.monoid.co.We’re adding code-scanning, consent management, and more in the coming weeks — hope to share them here as well. Feedback would be much appreciated!

A private AI assistant that uses open-source models and runs directly on your device. Your usage is completely anonymous, and your data never leaves your device. We do not track your credentials or monitor anything you do within the app. Privacy guaranteed.

Introducing Private AI Assistant: ensuring true anonymity by preventing fingerprinting and data collection. It mixes all AI requests, doesn't see web pages or addresses, and uses no user data. Experience ultimate privacy with Aloha's innovative solution.

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Hi all!Founder of Evervault[0] here.We're excited to launch our AI Privacy use case today, making it easy for developers to easily redact PII in unstructured data (like prompts) using a simple proxy.This means you can collect data from your users and have it redacted at the edge before it touches your infrastructure, or redact it before sending it to third-party LLM providers like OpenAI — solving a lot of the compliance and privacy pains facing developers today.I wrote more about our proxy approach on the Evervault Blog[1].Looking forward to hearing your feedback![0]: https://evervault.com[1]: https://evervault.com/blog/introducing-ai-privacy-relay-reda...

Let me ask you this, would you share sensitive data with a company without knowing if they can protect it?No, right?Because trust is missing. In order to trust them, you must assess their privacy and security postures.The problem is, that the current assessments process broken - it’s manual, repetitive and resource intensive.Annually, small and medium businesses spend an avg. 2.1 million euros on evaluating third parties, and companies being evaluated spend an avg. 1.9 million on completing those assessments. You can imagine that for enterprises, those numbers are much higher.As European regulations require vendor assessments and new global privacy laws are being aligned to the EU, this process will become even more challenging.hoggo is here to solve that with an AI powered solution that automates privacy and security assessments for both sides: Sellers can improve and showcase compliance and buyers can evaluate, manage and monitor them.Our platform is live and if you are facing compliance bottlenecks as sellers or having difficulties in assessing and monitoring your third-parties, we would LOVE your feedback and are open to pilot customers.