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- [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]

[^Benaloh]: Josh Daniel Cohen Benaloh, *Verifiable Secret-Ballot Elections*, Yale University Dissertation (1987) at https://www.proquest.com/openview/05248eca4597fec343d8b46cb2bef724/1?pq-origsite=gscholar&cbl=18750&diss=y.
[^Undeniable]: David Chaum and Hans van Antwerpen, "Undeniable Signatures" *Advances in Cryptology -- CRYPTO' 89 Proceedings* 435: 212-216 https://link.springer.com/chapter/10.1007/0-387-34805-0_20.
[^diff]: Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith, "Calibrating Noise to Sensitivity in Private Data Analysis", *Theory of Cryptography* (2006): 265-284.
[^Federated]: Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson and Blaise Aguera y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data" *Proceedings of the 20th International Conference on Artificial Intelligence and Statistics* (2017).

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.

Furthermore, these technical solutions increasingly intersect and integrate with a range of technical standards and public policies that support privacy. These include [cryptographic standards](https://csrc.nist.gov/projects/cryptographic-standards-and-guidelines) set by governments, privacy regulations and rights such as the EU [General Data Protection Regulation](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) and [standards for the inter-operation of encryption](https://www.eff.org/deeplinks/2022/04/eu-digital-markets-acts-interoperability-rule-addresses-important-need-raises).
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[^ZKCanon]: Elena Burger, Bryan Chiang, Sonal Chokshi, Eddy Lazzarin, Justin Thaler, and Ali Yahya, “Zero Knowledge Canon, Part 1 & 2,” _a16zcrypto_, September 16, 2022, https://a16zcrypto.com/posts/article/zero-knowledge-canon/.https://a16zcrypto.com/posts/article/zero-knowledge-canon/.
[^BowlingAlone]: Robert Putnam, “Bowling Alone: America’s Declining Social Capital,” _Journal of Democracy_ 6, no. 1 (1995): 65–78 and *Bowling Alone: The Collapse and Revival of American Community* (New York: Simon & Schuster, 2000).
[^PrivateClubs]: Pragmatist political philosophy Richard Rorty wrote "We can urge the construction of a world order whose model is a bazaar surrounded by lots and lots of exclusive private clubs." Richard Rorty, "On ethnocentrism: A reply to Clifford Geertz" *Michigan Quarterly Review* 25, no. 3 (1986): 533.
[^Benaloh]: Josh Daniel Cohen Benaloh, *Verifiable Secret-Ballot Elections*, Yale University Dissertation (1987) at https://www.proquest.com/openview/05248eca4597fec343d8b46cb2bef724/1?pq-origsite=gscholar&cbl=18750&diss=y.
[^Undeniable]: David Chaum and Hans van Antwerpen, "Undeniable Signatures" *Advances in Cryptology -- CRYPTO' 89 Proceedings* 435: 212-216 https://link.springer.com/chapter/10.1007/0-387-34805-0_20.
[^diff]: Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith, "Calibrating Noise to Sensitivity in Private Data Analysis", *Theory of Cryptography* (2006): 265-284.
[^Federated]: Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson and Blaise Aguera y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data" *Proceedings of the 20th International Conference on Artificial Intelligence and Statistics* (2017).

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