4 items
Looks for HEMs and other personal information sent to third parties. https://tincangit.github.io/hemandhaw-study/ About Hem and Haw: This browser extension monitors when hashes of your personally identifiable information (like emails or phone numbers) are sent to third parties. How it works: 1. You enter email addresses, phone numbers, and/or other details, and press submit. 2. Hem and Haw creates hashes of these inputs. 3. When a matching hash is detected being sent to a third party, Hem and Haw takes note and logs to our server: - Type of data (e.g., email), - Hash type (e.g., MD5), - Third party's hostname, - Originating website's hostname, - Where in the transmission (HTTP request) the hash was found, - HTTP Referer, - User Agent, - Whether you use an adblock, your self-reported browser and your self-reported privacy-consciousness score At any point, you can stop Hem and Haw from searching for hashes and add/modify/delete the PII to search for. Additionally, you can see the log of detected transmissions, download the log as JSON, and clear the log. You can use Hem and Haw to see where your hashed PII is being sent from and to! What Hem and Haw does NOT collect to our server: - Your actual personal data (e.g., email addresses or phone numbers) and its hashes, - HTTP request details and data, - Any other personal information. Your IP address is also not collected by our server. IMPORTANT NOTE: Hem and Haw will clear all results when you clear all history. Data collection is approved as part of a University of Calgary research study: REB25-0355. Note: As of now, the study is over. Data collection is currently disabled.
Dec 30, 2025
rating_count is the Chrome Web Store ratings count, not a written-review count.
Media assets
Screenshots and videos on the listing.
Has promo video
Whether the listing includes at least one video.
Languages
Declared language locales.
Developer website
Listing exposes a developer website URL.
Contact email
Listing exposes a contact email.
Keyword in name
Case-insensitive substring match in the name.
Keyword in description
Case-insensitive substring match in the description.
Keyword occurrences in description
Count of case-insensitive occurrences in the description.
Category user-count percentile
Share of same-category extensions with fewer users (null if unknown).
These are transparent listing completeness / keyword signals, not a prediction of Chrome Web Store search ranking.