A privacy centric matrix client

why

Syphon aims to be built on the foundations of privacy, branding, and user experience
in an effort to pull others away from proprietary chat clients to the matrix protocol.

We need to decentralize the web, but also provide a means of freedom within that system. Matrix has the potential to be a standardized peer-to-peer chat protocol, and in a way already is, that will allow people to communicate and transfer their data at will. Email has been standardized this way for a long time, as you can always email someone regardless of their provider. Most popular proprietary chat clients do not adhere to a publically available protocol and have too much control over users data.

Additionally, if the goal for Matrix is adoption to make instant messaging communication go the way of email, a network effect is required for this paradigm shift. Syphon makes the bet that the best way to attract new users is through strong branding and user experience. I hope that contributing and maintaining Syphon will help kick start this process and help those in need.

Syphon will always be a not for profit, community driven application.

features

• no analytics. period.
• no proprietary third party services
  • iOS will have APNS support, but will be made clear to the user
• all data is AES-256 encrypted at rest
• E2EE for direct chats using Olm/Megolm
  • group chats will be supported in the coming weeks
• all indicators of presence are opt-in only (typing indicators, read receipts, etc)
• customize themes and colors throughout the app

goals

• [ ] desktop clients meet parity with mobile
• [ ] screen lock and pin protected cache features
• [ ] P2P messaging through a locally run server on the client
• [ ] allow transfering user data from one homeserver to another, or from local to remote servers
• [ ] cli client using ncurses and the same redux store contained here (common)

getting started

You may notice Syphon does not look very dart-y (for example, no _private variable declarations, or using redux instead of provider) in an effort to reduce the learning curve from other languages or platforms. The faster one can get people contributing, the easier it will be for others to maintain or oversee a tool that does not exploit the user.

environment

• you’ll to do several things to setup the environment for Syphon
  • install flutter (stable channel - mobile / dev channel - desktop)
  • install android studio
  • install cmake version 3.10.2 - (for olm/megolm)
    • macos
    • linux
  • install libs needed for cmake
    • macos
      • brew install ninja
    • linux
      • sudo apt install ninja-build
  • clone repo and init submodules
    • git submodule update --init --recursive
  • run the following prebuild commands
    • flutter pub get
    • flutter pub run build_runner build

building

• ios and android should follow normal flutter building instructions
• linux:

1. add dependency overrides before running flutter pub get

dependency_overrides:
    dartx: ^0.3.0
    characters: ^0.3.0

2. run flutter pub run build_runner build to generate the hive mappings for state caches
3. comment out whats necessary to make the dependences appear like below

dev_dependencies:
  build_runner: ^1.0.0
  # flutter_test:
  #   sdk: flutter
  # hive_generator: 0.7.0

dependency_overrides:
  # dartx: ^0.3.0
  # characters: ^0.3.0

4. run flutter build linux && flutter build bundle
5. navigate to release at $SYPHON_ROOT/build/linux/release/bundle
6. Confirm build works with running $SYPHON_ROOT/build/linux/release/bundle/syphon

store (current)

• state (redux)
• state cache (redux_persist + hive)
• cold storage (hive)

store (future)

• state (redux)
• state cache (redux_persist + json_serializable + sembast)
• cold storage (sqlcipher)

store references

• hive
• sembast
• sqlcipher
• Redux vs. Bloc
• Redux Tutorial
• Redux Examples

local notifications (android only)

• utitlizes android_alarm_manager on Android to run the matrix /sync requests in a background thread and display notifications with flutter_local_notifications
• no third party notification provider will ever be used outside Apples APNS for iOS only

quirks

• fastlane is not used, it’s there for f-droid

assets

• paid icon
• iOS icons

generic references

• End-To-End Encryption implimentation guide from Matrix.org
• iOS file management flutter
• scrolling With Text Inputs
• multi-line text field
• keyboard dismissal
• changing transition styles
• animations
• adding a border without needing ctrl-p

decoration: BoxDecoration(
   border: Border.all(width: 1, color: Colors.white),
),

• understanding why olm chose the world ‘pickle’ for serialization, its from python

contributing

• email contact@syphon.org if you’d like to get involved. there’s a lot to do.
• donations are welcome, but not required. Syphon will always be a not for profit, community driven application not owned or sold by a corporation.

from those who made it possible

lub youu

Download Details:

Author: syphon-org

Demo: https://syphon.org/

Source Code: https://github.com/syphon-org/syphon

#flutter #dart #mobile-apps

What is GEEK

Buddha Community

Learning to Design a School Performance Matrix | Simpliv

Description
The module “Designing School Performance Matrix” figures towards identification of important aspects towards Quality Initiatives in the schools on ground of Quality Requisites as a priority. The course modulates the connect towards important dimensions to the schools with contributions and recognition required to each in particular. The aspects relate parenting, classroom management, beneficiary satisfaction and the contribution to teaching learning practices in totality.

Who is the target audience?

Educators/ Heads of Schools/ Researchers
Basic knowledge
Basic Requirement :

ICT Expertise
What will you learn
A Ready Reckoner Towards Effective School Management

ENROLL

#Designing School Performance Matrix #Learning to Design a School Performance Matrix | Simpliv #Matrix Courses #School Design Matrix

Google Location Tracking Lambasted in Arizona Lawsuit

The lawsuit, filed against Google by Arizona’s Attorney General, alleges that the tech giant uses “deceptive and unfair conduct” to obtain users’ location data.

Google has been hit by a lawsuit alleging that it violates user privacy by collecting location data via various means – and claiming that Google makes it nearly “impossible” for users to opt out of such data tracking.

The lawsuit, filed by Arizona Attorney General Mark Brnovich, alleges that Google uses “deceptive and unfair conduct” to obtain Android users’ location data via various applications, services and technologies, which is then used for advertising purposes. The alleged data collection would violate the Arizona Consumer Fraud Act, a set of laws that give protections to consumers in various transactions related to the sale or advertisement of merchandise.

“Google has engaged in these deceptive and unfair acts and practices with the purpose of enhancing its ability to collect and profit from user-location information,” according to the 50-page complaint, which was filed Wednesday in the Maricopa County Superior Court. “And profited it has, to the tune of over $134 billion in advertising revenue in 2019 alone. On information and belief, hundreds of millions of dollars of these advertising revenues were generated from ads presented to millions of users in the State of Arizona.”

Public consternation around Google’s data-collection policies was first set off by a 2018 Associated Press report, which claimed that Google services that are prevalent on both Android and iOS phones all store location data. The report alleged that Google would track users’ data even when they opt out of Google’s Location History feature, which collects data in order to personalize Google Maps.

This most recent lawsuit claims that Google’s alleged deceptive tactics extend beyond the issues with Location History highlighted by AP’s report. The redacted, public complaint claims that Google uses other means to bring in location data – including via Wi-Fi scanning and connectivity, diagnostic data and information from Google apps in “recent versions of Android.” This makes it impractical – and even impossible – for users to opt out of location tracking, the lawsuit alleges.

“Given the lucrative nature of Google’s advertising business, the company goes to great lengths to collect users’ location, including through presenting users with a misleading mess of settings, some of which seemingly have nothing to do with the collection of location information,” said the lawsuit.

According to Brnovich, these claims are based on both testimony from Google employees “given under oath” and from internal documents that were obtained from Google over the course of a nearly two-year investigation.

Google, for its part, argued against the claims and told Threatpost that it looks forward “to setting the record straight.”

#mobile security #privacy #android #arizona attorney general #data privacy #google #google lawsuit #location data #location data privacy #location history #mobile privacy

The Privacy of COVID-19 Apps — Reopening Alphaville

1. Three Privacy Sensitivity Levels of COVID-19 Apps

She nodded. I continued, “one way to evaluate contact tracing apps for their privacy sensitivity is to categorize them by the types of data they collect.” That is a reasonable first proxy, I thought, because what is not collected cannot be lost, misused, or compromised. Though, certainly, there are other criteria, such as whether contact tracing is centralized or decentralized, who is collecting the data, or how long it is retained. “On the sensitive end of the spectrum we have apps that are collecting personal data, such as e-mail addresses or phone numbers. The Healthy Together app, from the small social network company Twenty used in Utah [2], is an example. Once a user shows symptoms, they can be asked directly with whom they interacted previously. This approach is very effective but requires the exchange of personal data. Healthy Together collects data on a voluntary basis and explains their practices in a privacy policy [3].” The mayor nodded again, though, she seemed not quite convinced that this would be the right approach for Alphaville.

![A diagram outlining the three levels of privacy sensitivity; from high to low: personal data, location data, Bluetooth data.]

The three different levels of privacy sensitivity of COVID-19 apps.

I went on, “other apps are only relying on location tracking. GPS can be accurate up to a few centimeters.” I knew that HowWeFeel uses that option with anonymous user identifiers [4]. This app was developed by a team of scientists and Pinterest co-founder and CEO Ben Silbermann. It is recommended by the Governor of Connecticut [5]. Likely people here in Middletown, where I live, are using it. So, maybe that was something of interest. But as the mayor showed no reaction I continued, “at the least sensitive end of the spectrum we have apps that are just using Bluetooth beacons to detect whether two phones are in proximity. Bluetooth will not keep track of locations,” I said. “Imagine two unrelated people, Michael and Ralf, standing side by side at a bus stop. The sensors in Michael’s and Ralf’s phones are just picking up a random string of characters from each other. If Michael shows symptoms of the disease, he can upload his string to a server. All other phones, including Ralf’s, are periodically downloading the strings from there. Once Michael’s string matches the string already on Ralf’s phone, Ralf is notified that he was in contact with a symptomatic person. This form of contact tracing is used in Apple’s and Google’s ExposureNotification [6]. It is the least privacy sensitive approach.” The mayors face lit up. “That’s great,” she said, “but do you think it will actually work?” “That’s a good question,” I replied. As the mayor had other work to do, we agreed that I would research this question and we would touch base again in a few days.

#mobile-apps #privacy-technologies #privacy-protection #privacy #covid19

Artificial Intelligence and Online Privacy: Blessing and a Curse

Artificial Intelligence (AI) is a beautiful piece of technology made to seamlessly augment our everyday experience. It is widely utilized in everything starting from marketing to even traffic light moderation in cities like Pittsburg. However, swords have two edges and the AI is no different. There are a fair number of upsides as well as downsides that follow such technological advancements.

One way or another, the technology is moving too quickly while the education about the risks and safeguards that are in place are falling behind for the vast majority of the population. The whole situation is as much of a blessing for humankind as it is a curse.

In this article, we will be mainly discussing how the AI is being utilized, how the ease of processing data-enabled companies and government agencies to have power over online privacy, and how to stay careful of possible abuses of said power.

#data-privacy #privacy #artificial-intelligence #security #cybersecurity #online-privacy #ai #ml

Reasons Why Data Privacy Matters

Data privacy has been all the talk in the tech sector as of late. With the emergence of smartphones over a decade ago, our entire lives have been put online. Our behaviors and thoughts have been monitored not just through Facebook status updates, but through applications and browser tracking page visits, link clicks, and google searches. Everything we do on our phones is recorded and collected as data used for a variety of purposes from personal safety to advertising. In recent months, data privacy, or rather a lack thereof, has come to the forefront of tech conversations. With Apple launching an increased effort to protect users’ privacy, the personal data world as we know is about to change.

Not Your Mom’s Cookies

Have you ever wondered how your ads on various web pages know exactly what you like? Or how Amazon knows exactly what purchase to suggest next? All of this is due to data collected on your phone that goes by the term cookies. A cookie is a small text file from a website you visit that attaches to your browser.

This cookie contains information about you like your sex, age, location, email address, and other personal information. Marketers and advertisers can use this information to push target advertisements and content catered to you and your preferences. As consumers, we use cookies for more than we think we do, and they can actually be quite convenient. Your computer uses cookies when it auto-fills personal information when you’re checking out online or when it remembers which web pages you typically visit.

Cookies in 2021

While cookies have been around for quite some time, users have begun to question just how much data they have access to. There has been a recent push in protecting user data and data privacy. Because of this, tech giants like Apple and Google have taken steps to reduce the amount of data applications and browsers have access to. Their smartphones now prompt users to choose which platforms are allowed to track their online behaviors.

This severely limits the access that businesses and advertisers can have to large sums of personal data. So you might be wondering, is increased data privacy all good? Like all things, it has its upsides and downsides and boils down to personal preference.

Pros of Increased Data Privacy

1. Increased Security. For the most part, the more privacy you have, the more secure you are. By keeping a majority of our data private, personal information that may contain sensitive content is less likely to be spread. This is particularly true for saved bank accounts, credit card numbers, and even medical information.
2. Increased Transparency. When businesses have to request access to certain sets of information, as users we are more aware of what they need. Before increased data privacy, it was unclear just how much content these organizations were gaining access to. With these extra filters in data tracking, where our data goes is far more transparent.
3. Protection of Children. Around the world, a child accesses the internet every half second. While they’re online, they may visit chat rooms, surf the web, or go on social networking sites. Being online makes them vulnerable to cyberbullying, predators, or inappropriate content. Limiting access to their personal information can help them avoid falling victim to any of these threats.

#data #privacy #data-privacy #data-protection #cookies #internet-data-privacy