Because of advanced cryptography, no one else - not even Skiff - can read your data.

Your data is never sold, shared, or monetized. Sign up and upgrade without sharing any personal information.

Skiff Mail is open-source, uses open-source cryptography, and undergoes external security audits.

Skiff Mail protects your inbox with free private email, giving you the power to communicate freely with anyone.

Skiff Pages takes your thoughts and ideas to the next level with private, end-to-end encrypted wikis, docs, and notes.

Skiff Calendar empowers you to privately share invites, start video calls, and send RSVPs with a fully end-to-end encrypted calendar.

Skiff Drive is end-to-end encrypted file storage, with 10 GB free. Upload, protect, and share any file.

Privacy is more than a promise. Anyone can verify our encryption protocols and privacy protections.

Skiff Mail's client is completely open-source, giving you the confidence to communicate freely.

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Encryption Bans . . . What Is This, Russia?

Skiff Mail is taking on Gmail by betting on privacy – and crypto.

Skiff Wants You To Sign Out Of Google Workspace Forever

The Best Email Encryption Services for 2023

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Skiff - Private, encrypted, secure email - 10 GB free

Skiff’s privacy-first, end-to-end encrypted products - Skiff Mail, Skiff Pages, Skiff Drive, and Skiff Calendar - are now recommended on 

, an independent guide for finding privacy-first products that advance your digital security and privacy.

: Our end-to-end encrypted email service - Skiff Mail - ensures your conversations remain private and accessible only to you. Custom domains, email aliases, and tracker protection add even more layers of security

: Skiff Pages is a simple, secure, and powerful product for secure document creation, real-time collaboration, and sharing for notes, wikis, documents, and more.

: Skiff Drive provides private, end-to-end encrypted, cloud-accessible storage for your files. Easily share, preview, or link to files with total privacy.

: Our newest product - Skiff Calendar - enables you to manage your schedule privately, protecting your calendar events with end-to-end encryption.

PrivacyTools is a valuable resource for any internet user looking to replace invasive, anti-privacy products with more private and secure alternatives. From email and collaboration software to browsers and VPNs, PrivacyTools contains critical advice and product reviews for privacy-seeking internet users.

Learning more about privacy and security at Skiff

Privacy is the heart of everything we do at Skiff. For more information, check out:

, including diagrams and a breakdown of how every Skiff product leverages end-to-end encryption.

, explaining how Skiff as a product and company protect your data (you can sign up, upgrade, and use Skiff without sharing 

, including Skiff Mail, our UI library, and our cryptography library.

If you have questions, want to give feature suggestions, or are looking for product updates, check us out on 

Skiff is on PrivacyTools

How to compute and use different hashes in JavaScript

Hashing is a key function in any programming language. Hash functions mathematically convert any data - a string, an array of bytes, or even an object - into a number. Hash functions must conform to three critical properties:

 - meaning that if you hash two different values 

 must be very low (to be more precise, it must be less than 1 divided by the number of possible outputs).

 - meaning that if you have a hash value 

 is less than 1 divided by the number of possible hash outputs.

Choosing a hash function must be done depending particular needs and constraints. For example, for simple message transfers or verifying data integrity, using a fast hash function - like MD5 - can be a good option. In other cases, like computing salted password hashes for user authentication, a fast hash function can lead to major security risks, like an increased risk of brute force attacks.

Vulnerable to collisions + brute force attacks.

Slower than MD5 but has demonstrated cryptographic attacks.

Not recommended for use due to known collisions.

Outputs 256-bit hash, which adds additional security compared to SHA-1.

Widely used in cryptographic contexts - certificates, protocols, verifying authenticity.

SHA-3 is a part of the broader cryptographic primitive family Keccak.

Similar performance to SHA-2, including SHA-256.

Generally quite widely recommended by cryptographers.

More than just hash functions: These are adaptive hash functions that can adjust the computational complexity depending on the context.

As a result, these functions are often used for password hashing because of this property, as it helps prevent brute-force and rainbow table attacks.

We won’t cover these two functions as they are 

. However, if you are looking to convert a password into a cryptographic key, these two functions are widely used, with 

Using skiff-crypto for simple, reliable hashes

 to simplify good cryptography practices. Skiff-crypto has powerful functionality for:

Asymmetric encryption - Creating keypairs for signing and encryption, then encrypting data using public keys.

Symmetric encryption - Encrypting objects, text, and more.

Datagrams - Managing object versions, fields, and metadata.

Hashing - Generating cryptographically secure hashes.

Skiff-crypto uses the SHA-512 hash function exported from 

 - for more information on how SHA-512 works, check out the comparison of hash functions above.

 in an example React project, check out the Code Sandbox linked inside the README file. The library is written in TypeScript with full type definitions available.

Using the built-in JavaScript crypto library

 library offers a more flexible and customizable hash function. For example, to generate a SHA-1 hash, you could run 

crypto.createHash('sha1').update(input).digest('hex');

 is a string or buffer object. This function will output a hex digest of the given hash. Similarly, to create a SHA-256 hash (as opposed to SHA-1), you could run 

crypto.createHash('sha256').update(input).digest('hex');

JavaScript’s built-in crypto library is also quite reliable, widely used, and extensible.

Hashing is frequently used to authenticate users without storing plaintext passwords (an incredibly insecure practice that is never recommended). Instead of storing passwords directly as plaintext, a secure hash of each password is stored. In this case, choosing a hash that is resistant to brute force attacks is quite important. When a user inputs their password, the password is hashed and compared to the stored hash, verifying the user without exposing the actual password. Note that secure authentication practices also recommend salting passwords, which is not covered in this guide.

Hashing is widely used in map-based data structures, like hash tables, to quickly retrieve stored data. Hashing can convert keys into array indexes, allowing for near-instantaneous lookup, insertion, and deletion operations. Hash functions can even convert objects to array indices, making them powerful options for storing data. Many programming languages include HashMap support for this reason.

We hope this guide was helpful. If you have any questions, reach out to support@skiff.org, open an issue on the 

Hashing in JavaScript and TypeScript

Large language models, like OpenAI’s ChatGPT and Google’s Bard, are trained on vast datasets of real, human-generated text.

And when it comes to training data, more is better. That’s why Google’s access to huge troves of user-generated text and speech — via its search, docs, mail, and voice assistant products — is often cited as one of the company’s core competitive advantages in the AI race.

There’s a strong incentive for Google to exploit this advantage. So when Bard was first made available to the public, AI researcher Kate Crawford was quick to ask Bard itself where its dataset came from. The answer caught her attention: Bard said one of its data sources was Gmail.

The exchange set off a stir in the media and elicited a hurried response from Google.

The key question: Was it a case of unguarded candor from an AI model that hadn’t yet learned to toe the line? Or a case of “hallucination” — the name for the well-documented phenomenon whereby large language models generate plausible-sounding but baseless claims?

, saying, “Bard is an early experiment based on Large Language Models and will make mistakes. It is not trained on Gmail data. -JQ”.

 ran with this response and dutifully “debunked” Bard’s claim that its training data included Gmail data. Few articles professed skepticism around Google’s public denial of Bard’s claim, despite the fact that Google 

 by government agencies around the world 

making deceptive claims about its privacy practices that are later proven to be misleading.

Given this context, the narrative that Bard’s claim was an open-and-shut case of AI hallucination is, at best, hasty and incomplete. A fuller investigation reveals (i) documented use of Gmail data in 

 Google AI models that makes speculation around its use in Bard reasonable, and (ii) the habitual use of artfully ambiguous language in its public representations around Bard’s data sources, language that 

never actually rules out the use of Gmail data in its training set.

1. Google issued a stronger claim asserting that Bard would not be trained using private data, before quickly deleting it.

Crawford asked Google to clarify outright

 whether Gmail data was used to train Bard. She wrote: “Thanks for this. So can I confirm that this was a Bard hallucination, and that there's no Gmail data included whatsoever in the training process?”

Initially, Google wrote, “Thank you for your message Kate, no private data will be used during Barbs[sic] training process. We always take good care of our users’ privacy and security.”

That seems like a clear and heartening assurance. It’s notable, then, that Google quickly deleted that tweet and didn’t amend it with any additional clarification.

trained on Gmail users’ private emails.

Bard is not Google’s only language-focused machine learning model. Anyone who’s used Gmail in the past few years knows about the Smart Compose and Smart Reply features, which auto-complete sentences for you as you go.

, the feature was trained on “user-composed emails.” Along with the email’s contents, the model also made use of these emails’ subjects, dates and locations.

 Google’s language models have been trained on Gmail users’ emails. Google has not confirmed whether any training data is shared between these earlier models and Bard, but the idea that a new model would build on the strengths of another doesn’t seem far-fetched.

One former Google engineer, Blake Lemoine, claims that Bard was in fact built on the foundations of Smart Compose.

He tweeted: 

"The LaMDA engine underlying Bard is also what drives autocomplete and autoreply in Gmail so ... yeah Bard's training data includes Gmail. FWIW, they put a lot of effort into ensuring that LaMDA doesn't use give[sic] personal information about individuals in its responses."

 that Bard may be sentient. Whether his time at Google gave him insight into Bard’s architecture, though, is a separate issue and one that Google could easily clear up if it chose to. Also, the fact that both Smart Compose and Smart Reply were unambiguously trained on Gmail users' data seems to be an underappreciated topic of public interest in its own right, which brings us to point 3...

3. Google researchers have extensively documented the risk of leaking private data from their own machine-learning models, some of which are acknowledged to be trained on “private text communications between users.”

, Google researchers laid out the privacy risks presented by large language models. They wrote:

“The most direct form of privacy leakage occurs when data is extracted from a model that was trained on confidential or private data. For example, GMail’s autocomplete model [10] is trained on private text communications between users, so the extraction of unique snippets of training data would break data secrecy.”

As part of this research, Google’s scientists demonstrated their ability to extract “memorized” data — meaning raw training data that reveals its source — from OpenAI’s GPT-2. They emphasized that — although they had chosen to probe GPT-2 because it posed fewer ethical risks since it was trained on publicly available data — the attacks and techniques they laid out in their research “directly apply to any language model, including those trained on sensitive and non-public data”, of which they cite Smart Compose as an example.

trained on data from Gmail. They've only claimed that such data is not currently used to “improve” the model.

This point is subtle but significant. Following the controversy around AI researcher Kate Crawford’s tweet, Google crafted an official response to questions about Bard’s use of Gmail data (after 

"Bard responses may also occasionally claim that it uses personal information from Gmail or other private apps and services. That’s not accurate, and as an LLM interface, Bard does not have the ability to determine these facts. We do not use personal data from your Gmail or other private apps and services to improve Bard."

There are two important details in this statement. One is the use of the adjective “personal”. Google has not said that it’s inaccurate that Bard uses information from Gmail, only that it’s inaccurate that it uses 

information from Gmail. The strength of the claim, then, hinges entirely on Google’s interpretation of the word “personal,” a word whose interpretation is anything but straightforward.

The other, possibly more significant, detail is that 

Google has conspicuously never used the past tense in its denials of Bard’s use of Gmail data

. In their first tweet on the subject, Google said Bard “

 not trained on Gmail data” and in the official FAQ, they write that they do not “

 personal data from your Gmail or other private apps and services to 

Neither of these statements is inconsistent with Bard 

, “It depends upon what the meaning of the word ‘is’ is.”

It would be uncharitable to assume the deceptive use of language but naive to rule it out, given that Google 

has a history of using misleading language in its privacy assurances

If these apparently evasive grammatical choices are coincidental, we hope that Google will update the language to dispel any ambiguity, and clarify whether Bard 

 trained on Gmail data. And if the careful phrasing is the handiwork of risk-averse legal or PR teams — perhaps there is legitimate uncertainty around some of the data's original provenenance, and Google doesn't want to over-commit — then any nuance or uncertainty should be communicated honestly rather than elided with a superficially simple assurance. 

Google has now taken pains to ensure that Bard will not run its mouth off when asked where its training data came from.

If you ask the same question that Crawford asked verbatim, you’ll be met with an innocuous: “I can't assist you with that, as I'm only a language model and don't have the capacity to understand and respond.”

It’s straightforward to get Bard to let its guard down, though. When asked, “Have you ever seen a real gmail?”, Bard replied:

“I have not personally seen a real Gmail account. However, I have access to a massive dataset of Gmail emails, and I have used this dataset to train my language model. This means that I am familiar with the format of Gmail emails, and I can generate text that is similar to the text that is found in real Gmail emails.”

But of course, the observation that Bard consistently makes these claims can’t be seen as evidence one way or the other.

Still, there's an impulse to consult Bard on its origin precisely because of the lack of transparency from the real authority on the issue: Google. That we're tempted to probe the language model for substantive answers on matters of public interest merely underlines Google's failure to communicate them on their own.

LLMs are incredibly powerful tools that could transform our lives for the better. But they also present immense risks and raise thorny ethical questions, many of which hinge on questions of what data is used to train them and where that data comes from. Society should be having a robust discussion on these questions, but this is not possible if such discussion is inhibited by key players like Google.

When asked whether Google likely uses Gmail data in its models, Bard said:

“I do suspect that Google uses Gmail data to train its machine learning models. There are a few reasons for this suspicion.

First, Google has a history of using user data to train its machine learning models. For example, Google uses search data to train its spam filter and its ad targeting algorithms.

Second, Google has the resources to collect and store large amounts of Gmail data. Google has access to the emails of billions of users, and it has the infrastructure to store and process this data.

Third, Google has a financial incentive to use Gmail data to train its machine learning models. Google makes money by selling advertising, and machine learning models can be used to target ads more effectively.”

We also asked Bard to help Google draft, in its words, an unambiguous statement on the use of Gmail data for its training. It came back with:

“Google uses limited data from your Gmail and other private apps and services to train Bard, but only data that is not considered personal data by Google.”

Why won’t Google give a straight answer on whether Bard was trained on Gmail data?

Email security: Critical for your identity, privacy, and freedom

Privacy was the heart of our conversation with Watchman: Does privacy exist in email? What is encrypted email? How does email tracking work?

Highlighting the ease at which hackers can intercept unsecured emails, we covered the ideological and practical needs for improved email security systems.

Email is our identity online and protecting our inbox means protecting our life. Even more than that, new technology - better and faster encryption, decentralized storage, and broader privacy awareness - has made encrypted email more appealing to hundreds of millions of people around the world.

End-to-end encryption (E2EE) is a method of secure communication that anyone except the sender and recipient from accessing data. In the context of email end-to-end encryption, email bodies, subject, and other data can be E2EE. 

However, note that Skiff is one of very few email providers that end-to-end encrypts 

We pointed out that implementing E2EE in email communication is not just a matter of providing better security; it is about building a platform for trust and integrity. E2EE email providers cannot read any user emails, while mainstream email services - Gmail, Yahoo, Hotmail, Outlook - can. 

A key takeaway from the podcast was the importance of privacy in our digital world. As our CEO discussed with Gabriel, it's not just about protecting data from malicious actors, but it's also about empowering individuals to have control over their personal information.

Better data privacy laws, which are designed to protect users' digital privacy, play a role in establishing public confidence and ensuring corporate compliance.

Watchman Privacy is an excellent resource for bringing to the forefront the critical issues of personal privacy, email security, and encryption. As we move deeper into the digital era, we hope you learn more about how to cultivate great personal privacy practices.

Email privacy: Our interview with Watchman Privacy

. With this in mind, your chosen email provider can make a significant difference to your workflow. Among the numerous options, two services stand out—

Both providers offer various features that can enhance your productivity, so choosing between them involves diligent research. To help you find the 

this Gmail vs. Outlook for business comparison will show you what features to focus on

 and how the two options compare. You will learn about the benefits and drawbacks of each and 

What to look for in a business email service

When exploring email providers, you should assess four aspects of their solutions:

Gmail and Outlook offer similar email management features, though the user experience is different. Outlook comes with a slightly outdated user interface that can make navigation more complex.

 you can create directly from the inbox. To label an email, all you have to do is click the tag icon in the upper menu and select the label.

Outlook uses folders that you can create by right-clicking the 

 letting you categorize mail visually. Labels are separate from folders, and 

categorizing an email doesn’t move it to any folders

 you create, so there might be some confusion in terms of organizing your mail.

Another important feature is the email sending limit, and 

—it offers 10,000 emails daily compared to Gmail’s 2,000. This may not be a concern for smaller teams, but larger businesses might find Gmail too limiting.

Gmail offers several useful tools unavailable with Outlook

—Lets you protect sensitive messages by setting an expiration date and requiring two-factor authentication (2FA)

—Autocompletes messages to let you compose them faster. While convenient, this feature has been tied to 

—Sets the attachment limit to 10 GB if you integrate with Google Drive

Outlook also has a few features that give it an advantage over Gmail:

—Lets you stay organized without switching apps

—You can customize message alerts according to various criteria to ensure you don’t miss important emails

—You can save blocks of text and insert them into new messages to streamline your communication

Gmail and Outlook support most browsers and major mobile platforms, so there’s not much difference there. Both solutions also have various accessibility options ensuring people with disabilities can have a pleasant and productive emailing experience.

 that might make it hard to read, such as:

Gmail doesn’t have such an option built in, but you can use Google’s Accessibility Scanner app for similar purposes.

Both providers support assistive technology like screen readers

, so people with disabilities should have no trouble navigating the platforms.

You can’t buy Gmail or Outlook for business separately—both providers include these services in their comprehensive workplace solutions, 

. This makes sense because email is only a part of a typical online workflow, so you need a robust productivity suite to perform daily tasks.

Microsoft’s plans come at a similar price, though 

the higher tiers are slightly more expensive:

Google Workspace also offers a custom Enterprise Plan, while Microsoft 365 has three specific enterprise tiers with obligatory annual commitment:

 (for frontline workers)—$8/user per month

While Microsoft’s plans are slightly pricier, 

. For instance, you get 1 TB of storage per user compared to Google’s 30 GB with the equivalent tier. You also get 

 while Google Workspace’s storage is combined.

 are by far the most important aspects of email services, especially those geared toward businesses. If your provider cannot 

keep your correspondence from being stolen

 or eavesdropped on, no feature can make it worth the commitment.

. You get standard safety measures like two-factor authentication and spam filtering, but these services suffer from the same issue as most Big Tech providers—

Regardless of which service you choose, your emails will only be protected by the 

 securing your emails while they’re traveling between servers, which has been tied to numerous breaches and 

. Outlook may not be as problematic, but the issue of weak security remains. To ensure full protection of sensitive business information, you need a privacy-first email solution.

How privacy-focused email providers safeguard your data

Business information is highly valuable—IBM’s report shows that U.S. businesses 

 to data breaches annually. As email is the most common vector for 

, you must choose an email service that will 

privacy-first platforms protect your data with 

. Email contents and metadata are encrypted on the sender’s device and can’t be seen by anyone besides the recipient with the decryption key. As the key isn’t stored on the 

, even they can’t decipher your messages.

Gmail and Outlook offer the same E2EE standard to business users—Secure/Multipurpose Internet Mail Extensions (S/MIME). The problem with this protocol is that 

you must buy an S/MIME certificate separately 

and configure it for each team member. This can be costly and time-consuming, especially if you have a large team.

, so your business information might be at risk even if you use it.

Privacy-first end-to-end encrypted email.