Andrei Lapets

[Andrei Lapets @andreilapets]

Once the ecosystem has gained experience from building many such ensembles (and filtered out the ones that either aren't a fit for popular use cases or do not offer benefits users value), everyone can begin factoring and identifying the common ones. Those will become the building blocks that future PET compilers emit.

[Andrei Lapets @andreilapets]

This commentary captures well Nillion's position with regard to relying on any specific PET. Each differs from the others not only in performance but (sometimes subtly and sometimes dramatically) in the privacy benefits it can add to a use case. In fact, we have learned over the years that most PETs are qualitatively a poor fit for most use cases! We believe custom-built PET ensembles are the only viable way to address use cases for at least the next few years.

[Andrei Lapets @andreilapets]

Multi-party computation is a compelling example that could support this paper's argument. While a secret-shared lookup table split over two machines light-years apart can reply intelligently to queries from a midpoint, the machines' contents would be seen locally as random noise.

[Alexander Lerchner @AlexLerchner]

I highly recommend reading The Blind Spot for anyone interested in why observer-independent computation is a category error.

And if you haven't read the preprint yet, you can find it here: https://t.co/Q4poM9HtO0

[Andrei Lapets @andreilapets]

Privacy-enhancing technologies will not be worthwhile differentiators if users can't confirm apps use them and apps can't credibly advertise them as a feature. Starting today, anyone motivated to help address this gap and to make PETs more competitive can run a Blacklight node.

[Nillion @nillion]

Nillion Blacklight is a decentralized network that verifies private workloads running on the Blind Computer.

It checks that workloads are running correctly and honestly inside secure enclaves (TEEs), without revealing user data or execution logic.

This verification is done by Blacklight nodes.

[Andrei Lapets @andreilapets]

APIs transformed the developer experience by standardizing how apps share and leverage data. However, APIs only indirectly involve and benefit the users who subsidize this ecosystem by contributing their data. BPIs can act as a gradual path towards a more user-centric ecosystem.

[Build On Nillion @buildonnillion]

https://t.co/Av923AWuyo

[Andrei Lapets @andreilapets]

Nearly a decade after the vision was first articulated, a modular web-friendly set of PETs software tools that lets developers disentangle component roles/functions in apps and workflows (thereby allowing them to minimize exposure of user data) is taking shape at @nillion.

[Build On Nillion @buildonnillion]

https://t.co/B4mZGL2JCE

[Andrei Lapets @andreilapets]

@celestineia The irony that this is a 4K video...

[Andrei Lapets @andreilapets]

@CryptoMeina @juanaxyz00 Even if we're doing our own farming, we can't avoid the PFAS in the groundwater.

[Andrei Lapets @andreilapets]

@CryptoMeina @juanaxyz00 Targeted advertising campaigns created in response to the online/offline activity of millions of people can already cause feedback loops that cause them to buy unhealthy food products, for example. Now this can happen even more quickly and with even more steps automated.

[Andrei Lapets @andreilapets]

One thing not explicit in this discussion is the privacy risk of a prompt for *others*: family members, friends, colleagues, and acquaintances. We may trust an individual to make informed choices about themselves based on risk feedback (though humans clearly have trouble with this), but do they trust everyone around them to do the same? Do they trust themselves to have the best interests of others in mind when evaluating such feedback?

[Andrei Lapets @andreilapets]

Even supposing privacy feels unimportant at the individual level, there is the question of how this impacts society collectively. We can't be sure there isn't a way to exploit aggregations of small, innocuous facts about the daily routines of millions of people. Without private spaces (to do mundane, unusual, or subversive things), society as a whole becomes a more homogenous and less diverse system, potentially too fragile to handle unforeseen disruptions (including deliberate or malicious manipulation).

[Andrei Lapets @andreilapets]

The latest release of @iabtechlab's PAIR standard addresses industry feedback and improves interoperability with digital advertising solutions like @prebidorg, showing that #privacyenhancingtechnologies like #privatesetintersection are finding adoption in real-world workflows.

[IAB Tech Lab @IABTechLab]

#IABTechLab is excited to release the latest version of the open PAIR 1.1 protocol! This version clarifies the definitions of several of the terms in the protocol and improves the integration with prebid with a new Open PAIR module. Read more here: https://t.co/xbXTf7TIHN

[Andrei Lapets @andreilapets]

@punk6052 @jambutties @aztecnetwork @nillion Management of network nodes is not centralized at all. Developers can treat as a cluster any ad hoc combination of nilDB MPC nodes, whether operated by Nillion or external parties: https://t.co/cPgDtnDU8R. Nodes are not even aware of one another or where other secret shares sit.

[Andrei Lapets @andreilapets]

With large enterprises now contributing to the decentralization of @nillion-enabled infrastructure, developers can leverage #privacyenhancingtechnologies like #multipartycomputation and #homomorphicencryption with more confidence and flexibility. https://t.co/bXf5teFjVf

[Andrei Lapets @andreilapets]

It's great to see that the @FTC is examining #multipartycomputation and other #privacyenhancingtechnologies in enough detail to articulate some of the challenges associated with deploying them in production (and citing the @BU_Computing/@thebwwc wage equity effort as an example).

[FTC @FTC]

While an important tool to prevent intentional or accidental misuse of data, companies making representations about their use of privacy enhancing technologies must continue follow the law and ensure that any privacy representations are accurate: https://t.co/DCW9nBNpnJ

[Andrei Lapets @andreilapets]

@kayabaNerve @AvishaiY @nillion We are assembling a small interest group on the topic of non-interactive ITS MPC at https://t.co/4fiH1YatQV. Feel free to reach out if you think there is an overlap (or any related topics/ideas that would be exciting to discuss).

[Andrei Lapets @andreilapets]

@AvishaiY @nillion To evaluate an arbitrary circuit using Nillion's technique, the circuit would need to be converted to a possibly exponentially larger multivariate polynomial ("sum of products"). While this will not always scale, it is notable that this conversion adds no rounds of communication.

[Andrei Lapets @andreilapets]

@AvishaiY @nillion In particular, it is easier to see how Nillion's technique does not run afoul of known lower bounds if you consider it in the context of results such as this one: https://t.co/DnJXDq3XON