We tenatively postulated it would be fine to do this as long as seeing a name on your match page gave no more than like a 5:1 update about those people having checked you. 

I would strongly advocate against this kind of thought; any such decision-making procedure relies on the assumption that you correctly figure out all the ways such information can be used, and that there isn't a clever way an adversary can extract more information than you had thought. This is bound to fail - people come up with clever ways to extract more private information than anticipated all the time. For example:

• Timing Attacks on Web Privacy:
  • We describe a class of attacks that can compromise the privacy of users’ Web-browsing histories. The attacks allow a malicious Web site to determine whether or not the user has recently visited some other, unrelated Web page. The malicious page can determine this information by measuring the time the user’s browser requires to perform certain operations. Since browsers perform various forms of caching, the time required for operations depends on the user’s browsing history; this paper shows that the resulting time variations convey enough information to compromise users’ privacy.
• Robust De-anonymization of Large Datasets (How to Break Anonymity of the Netflix Prize Dataset)
  • We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world’s largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber’s record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.
• De-anonymizing Social Networks 
  • We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.
• On the Anonymity of Home/Work Location Pairs
  • Many applications benefit from user location data, but location data raises privacy concerns. Anonymization can protect privacy, but identities can sometimes be inferred from supposedly anonymous data. This paper studies a new attack on the anonymity of location data. We show that if the approximate locations of an individual’s home and workplace can both be deduced from a location trace, then the median size of the individual’s anonymity set in the U.S. working population is 1, 21 and 34,980, for locations known at the granularity of a census block, census track and county respectively. The location data of people who live and work in different regions can be re-identified even more easily. Our results show that the threat of re-identification for location data is much greater when the individual’s home and work locations can both be deduced from the data.
• Bubble Trouble: Off-Line De-Anonymization of Bubble Forms
  • Fill-in-the-bubble forms are widely used for surveys, election ballots, and standardized tests. In these and other scenarios, use of the forms comes with an implicit assumption that individuals’ bubble markings themselves are not identifying. This work challenges this assumption, demonstrating that fill-in-the-bubble forms could convey a respondent’s identity even in the absence of explicit identifying information. We develop methods to capture the unique features of a marked bubble and use machine learning to isolate characteristics indicative of its creator. Using surveys from more than ninety individuals, we apply these techniques and successfully reidentify individuals from markings alone with over 50% accuracy.

If "show people who have checked you" is a thing that would improve the experience, then I as a user would appreciate a checkbox "users who you have checked and have not checked you can see that you have checked them". I, for one, would check such a checkbox.

One level of privacy is "the site admins have access to data, but don't abuse it". But there are other levels, too. Let's take something I assume is private in this way, the identities of who has up/downvoted a post or comment.

• Can someone e.g. inspect the browser page or similar, in order to identify these identities?
• Can someone look through the Forum Magnum Github repo and based on the open source code find ways to identify these identities?
• Can someone on GreaterWrong identify these identities?

For other things that seem private (e.g. PMs), are any of those vulnerable to stuff like the above?

I just got a "New users interested in dialoguing with you (not a match yet)" notification and when I clicked on it the first thing I saw was that exactly one person in my Top Voted users list was marked as recently active in dialogue matching. I don't vote much so my Top Voted users list is in fact an All Voted users list. This means that either the new user interested in dialoguing with me is the one guy who is conspicuously presented at the top of my page, or it's some random that I've never interacted with and have no way of matching.

This is technically not a privacy violation because it could be some random, but I have to imagine this is leaking more bits of information than you intended it to (it's way more than a 5:1 update), so I figured I'd report it as a bug unanticipated feature.

It further occurs to me that anyone who was dedicated to extracting information from the system could completely deanonymize their matches by setting a simple script to scrape https://www.lesswrong.com/dialogueMatching every minute or so and cross-referencing "new users interested" notifications with the moment someone shoots to the top of the "recently active in dialogue matching" list. It sounds like you don't care about that kind of attack though so I guess I'm mentioning it for completeness.