FYI: The headings on your Careers page are all in French, e.g. "Qui sommes-nous ?
", but the actual content is all in English.
Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
31–40 of 50 posts
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#32FYI: The headings on your Careers page are all in French, e.g. "Qui sommes-nous ? ", but the actual content is all in English.
Thanks for catching it! will fix it.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#33If my model is used to profile a given user such as to maximize revenue from them (my objective is generally increasing with a more accurate classification of a user to the degree that such categories are revenue relevant), does this model still work?
If so, how is it privacy compliant, i.e. suffice the intent of the law in say, EU countries, or will not be identified as "privacy theater" in the US? If not, what do you do in these cases?
Cool to get your take on this.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#34Earlier quoted context omitted.
Is PySyft used under the hood?
No, we do not. Pysyft was mostly first designed to do federated learning. Sarus targets organizations that have their data in one central repository in a trusted curator model. It lets external data practitioners query that data with all sorts of data jobs (not just ML, but also SQL analysis, and spark soon).
side question: Is federated learning used in production? if not why?
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#35This sounds like a genuinely useful product with terrible marketing/copywriting on the landing page. Make your next hire in product marketing.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#36This sounds like a genuinely useful product with terrible marketing/copywriting on the landing page. Make your next hire in product marketing.
This person just joined last month! ;)
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#37If my model is used to profile a given user such as to maximize revenue from them (my objective is generally increasing with a more accurate classification of a user to the degree that such categories are revenue relevant), does this model still work? If so, how is it privacy compliant, i.e. suffice the intent of the law in say, EU countries, or will not be identified as "privacy theater" in the US? If not, what do y…
If the model training is designed to profile just one user, no, the model won't work by design. What you describe is an attack on the privacy of that user and we do want to make sure they fail.
The way differential privacy works with machine learning is that it guarantees that one given record cannot have a significant impact on the weights of the models and therefore on its performance. In the particular case of SGD-based models, the guarantee holds for every step of the descent. A good place to start on the topic is Abadi 2016 (https://arxiv.org/pdf/1607.00133.pdf).
What is important in the approach is that we don't need to detect that there is something funny in the loss function of the model. Sarus uses the exact same approach whether the model or the loss function is malevolent or not. The guarantees still hold. This is important because a lot of models can extract personal information even with no intention of doing so and no real way to detect it.
A good way to think about model performance is that we are looking for models that perform well irrespective of one record. If there are many users that have the same pattern of the user you are trying to spy on, the model may still be good but you won't know whether it's because of that user or not.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#38Earlier quoted context omitted.
No, we do not. Pysyft was mostly first designed to do federated learning. Sarus targets organizations that have their data in one central repository in a trusted curator model. It lets external data practitioners query that data with all sorts of data jobs (not just ML, but also SQL analysis, and spark soon).
side question: Is federated learning used in production? if not why?
No, we don't do federated learning at Sarus today. We operate in the trusted curator model: a party has a centralized database and lets external practitioner leverage it. This is the most common setup in the industry (think hospitals, health insurance companies, banks, streaming services...).
That being said, Sarus can be used to protect one node of a federated learning network. For instance each hospital could have a Sarus instance. The data scientist would need to take care of the orchestration of the nodes themselves but the Sarus API would make their life easy to interact with each data source, especially if all the sources are not identical.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#39If my model is used to profile a given user such as to maximize revenue from them (my objective is generally increasing with a more accurate classification of a user to the degree that such categories are revenue relevant), does this model still work? If so, how is it privacy compliant, i.e. suffice the intent of the law in say, EU countries, or will not be identified as "privacy theater" in the US? If not, what do y…
Sarus would typically fit in organizations that legitimately collect personal data and can take decisions based on these data.
In these cases you don't want (and most of the time cannot legally) let anyone in the organisation have access to the full personal data records. Using Sarus anyone, even untrusted parties can run analysis on your data safely. These analysis can be classification-model-fitting. You can then classify accurately users to maximize your revenue as long as you can observe the values to feed into your classifier.
Re: Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
#40Much needed. Great opportunity. Happy hunting.
> ...the API provides synthetic data samples with the same schema and statistical distribution by default
Neat. Repurposing test data generators. I like it.
> Our model is a software license to run on our clients’ cloud.
Just to confirm my understanding: Sarus never sees the client's data? Cool.
--
I'm fine with differential privacy. I haven't read those most recent papers, so I'm a little out of date.
That said...
Encrypting data at rest at the field level is an important missing piece from the future perfect privacy stack.
Just like how proper password vaults work. Salt, hash, encrypt. Never store the actual password.
The book Translucent Databases details clever examples of this strategy for misc use cases. Never store PII as plaintext.
Translucent databases and differential privacy are orthogonal. I have no ideas on how to productize (or SaaS-atize) translucent strategies.