Merge pull request pluralitybook#809 from gnomevan/patch-78

final attempt at these footnotes 4-2-association-and-⿻-publics.md

contents/english/4-2-association-and-⿻-publics.md

@@ -48,6 +48,7 @@ To motivate what this means to a game theorist, it may be helpful to consider wh
 [^Contextcomm]: That common knowledge is precisely the foundation of context against which communication must optimize is elegantly formally proven by Zachary Wojowicz, "Context and Communication" (2024) at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4765417.
 
 The importance of such higher-order knowledge for collective action is such a truism that it has made its way into folk lore.  In the classic Hans Christian Andersen tale of "[The Emperor's New Clothes](https://en.wikipedia.org/wiki/The_Emperor%27s_New_Clothes)", a swindler fools an emperor into believing he has spun him a valuable new outfit, when in fact he has stripped him bare.  While his audience all see he is naked, all are equally afraid to remark on it until a child's laughter creates understanding not just that the emperor is naked, but that others appreciate this fact and thus each is safe acknowledging it.  Similar effects are familiar from a range of social, economic and political settings: 
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 - Highly visible statements of reassurance are often necessary to stop bank runs, as if everyone thinks others will run, so will they.[^Runs]
 - Denunciations of "open secrets" of misdeeds (e.g. sexual misconduct) often lead to a flood of accusations, as accusers become aware that others "have their back" as in the "#MeToo" movement.[^MeToo]
 - Public protests can bring down governments long opposed by the population, by creating common awareness of discontent that translates to political power.[^Protest]
@@ -98,12 +99,12 @@ The field of [cryptography](https://en.wikipedia.org/wiki/Cryptography) has long
 
 Building on top of this foundation and branching out from it, a number of powerful privacy-enhancing technologies (PETs) have been developed in recent years.  These include:
 
-- [Zero-knowledge proofs](https://blog.cryptographyengineering.com/2014/11/27/zero-knowledge-proofs-illustrated-primer/) (ZKPs): these allow the secure proof of a fact without leaking the underlying data.  For example, one might prove that one is above a particular age without showing the full driver's license on which this claim is based.
-- [Secure multi-party computation](https://en.wikipedia.org/wiki/Secure_multi-party_computation) (SMPC) and [homomorphic encryption](https://en.wikipedia.org/wiki/Homomorphic_encryption): These allow a collection of individuals to perform a calculation involving data that each of them has parts of without revealing the parts to the others and allow for the process to be verified both by themselves and others.  For example, a secret ballot can be maintained while allowing secure verification of election results.[^Benaloh]
-- Unforgeable and undeniable [signatures](https://en.wikipedia.org/wiki/Undeniable_signature): These allow key controllers to sign statements in ways that cannot be forged without access to the key and/or cannot be denied except by claiming the key was compromised.[^Undeniable] For example, parties entering into a (smart) contract might insist on such digital signatures just as physical signatures that are hard to forge and hard to repudiate are important for analog contracts.
-- [Confidential computing](https://en.wikipedia.org/wiki/Confidential_computing): This solution to similar problems as above is less dependent on cryptography and instead accomplishes similar goals with "air gapped" digital systems that have various physical impediments to leaking information.
-- [Differential privacy](https://en.wikipedia.org/wiki/Differential_privacy): This measures the extent to which disclosures of the output of a computation might unintentionally leak sensitive information that entered the calculation.[^diff]  Technologists have developed techniques to guarantee such leaks will not occur, typically by adding noise to disclosures.  For example, the US Census is legally required both to disclose summary statistics to guide public policy and keep source data confidential, aims that have recently been jointly satisfied using mechanisms that ensure differential privacy.
-- [Federated learning](https://en.wikipedia.org/wiki/Federated_learning): Less a fundamental privacy technique than a sophisticated application and combination of other techniques, federated learning is a method to train and evaluate large machine learning models on data physically located in dispersed ways.[^Federated]
+- [Zero-knowledge proofs](https://blog.cryptographyengineering.com/2014/11/27/zero-knowledge-proofs-illustrated-primer/) (ZKPs)- these allow the secure proof of a fact without leaking the underlying data.  For example, one might prove that one is above a particular age without showing the full driver's license on which this claim is based.
+- [Secure multi-party computation](https://en.wikipedia.org/wiki/Secure_multi-party_computation) (SMPC) and [homomorphic encryption](https://en.wikipedia.org/wiki/Homomorphic_encryption)- These allow a collection of individuals to perform a calculation involving data that each of them has parts of without revealing the parts to the others and allow for the process to be verified both by themselves and others.  For example, a secret ballot can be maintained while allowing secure verification of election results.[^Benaloh]
+- Unforgeable and undeniable [signatures](https://en.wikipedia.org/wiki/Undeniable_signature)- These allow key controllers to sign statements in ways that cannot be forged without access to the key and/or cannot be denied except by claiming the key was compromised.[^Undeniable] For example, parties entering into a (smart) contract might insist on such digital signatures just as physical signatures that are hard to forge and hard to repudiate are important for analog contracts.
+- [Confidential computing](https://en.wikipedia.org/wiki/Confidential_computing)- This solution to similar problems as above is less dependent on cryptography and instead accomplishes similar goals with "air gapped" digital systems that have various physical impediments to leaking information.
+- [Differential privacy](https://en.wikipedia.org/wiki/Differential_privacy)- This measures the extent to which disclosures of the output of a computation might unintentionally leak sensitive information that entered the calculation.[^diff]  Technologists have developed techniques to guarantee such leaks will not occur, typically by adding noise to disclosures.  For example, the US Census is legally required both to disclose summary statistics to guide public policy and keep source data confidential, aims that have recently been jointly satisfied using mechanisms that ensure differential privacy.
+- [Federated learning](https://en.wikipedia.org/wiki/Federated_learning)- Less a fundamental privacy technique than a sophisticated application and combination of other techniques, federated learning is a method to train and evaluate large machine learning models on data physically located in dispersed ways.[^Federated]
 
 It is important to recognize two fundamental limitations of these techniques that depend most on cryptography (especially the first three); namely they depend on two critical assumptions. First, keys must remain in the possession of the desired person, a problem closely related to the identity and recovery questions we discussed in the previous chapter.  Second, almost all cryptography in use today will break and in many cases its guarantees be undone by the advent of quantum computers, though developing quantum resistant schemes is an active area of research.
 
@@ -114,6 +115,7 @@ Yet a basic limitation of almost all this work is the focus on protecting commun
 [^overshare]: danah boyd, *It's Complicated: The Social Lives of Networked Teens* (New Haven, CT: Yale University Press, 2014). Dave Eggers, *The Circle* (New York: Knopf, 2013).
 
 The basic problem is that while most cryptography and regulation treats privacy as about individuals, most of what we usually mean when we talk about privacy relates to groups.  After all, there is almost no naturally occurring data that pertains to exactly a single individual. Let's revisit some of the examples of the social life of data from the previous chapter.
+
 - Genetic data: genes are, of course, significantly shared in a family, implying that the disclosure of one individual's genetic data reveals things about her family and, to a lesser extent, about anyone even distantly related to her.  Related arguments apply to many medical data, such as those related to genetic conditions and transmissible diseases.
 - Communications and financial data: communications and transactions are by their nature multiparty and thus have multiple natural referents.
 - Location data: few people spend much of their time physically distant from at least some other person with whom they have common knowledge of their joint location at that moment.