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WHAT’S “CAN’T BE EVIL”?

When two or more parties are trying to collaborate around data, traditional Data
Protection solutions do not ensure that a user of the data cannot abuse the
data. There are however ways to provably ensure that data abuse cannot occur.



DATA PROTECTION ≠ DATA PRIVACY

Data Protection and Data Privacy are distinct problems requiring different
solutions.


WHAT IS DATA PROTECTION?

Data protection implies protecting your data from other parties that have access
to the same resources. For example, in a shared server context where a cloud
vendor is allowing multiple parties to rent partial virtual access to the same
physical hardware, data protection solutions are essential to make sure that
sensitive data being processed is not exposed to other renters of that same
physical hardware.

Typical data protection solutions are Confidential Compute solutions (like
TEEs), Homomorphic Encryption and Federated Learning. Confidential Compute-based
solutions use an isolated piece of hardware to ensure that the memory accessed
by an application is not accessible by others. Homomorphic Encryption-based
solutions keep data protected by ensuring that the data is never in a raw form –
so the data is nonsensical to anybody else except the authorized application.
Federated Learning is another solution that ensures that an AI model is trained
on data that is in different locations without bringing it together.

However, data protection solutions don’t take or enforce an opinion on how the
data ought to be used. This is important because data protection solutions
cannot enforce any regulatory or compliance-driven requirements on how the data
must be used. Any application can use data protection solutions to process data,
including malicious applications. For example, even a virus can run inside
Confidential Compute chip, with full isolation from observation by an
anti-virus. Using homomorphic encryption solutions, a nefarious program can
claim to be processing patient data to identify if a patient has a particular
disease, but can actually process that data to identify the patient’s
real-identity, which is a violation of HIPAA. Similarly, a user of Federated
Learning, can claim to be training a model for disease screening, but encode the
patient’s identifiable biometric information into the model.

For a real-world example of Data Protection solutions not protecting the privacy
of people data, the data can be cross-referenced with other information to
identify specific individuals. Netflix published an anonymized data set with 100
million records of customers’ movie ratings, challenging citizen data scientists
to use it to develop new recommendation algorithms. Researchers were able to
identify >84% of the individuals in Netflix’s ‘anonymized’ dataset by
cross-referencing it with another one from movie ranking site IMDb.

To summarize, Data Protection solutions protect the data from observation.


WHAT IS DATA PRIVACY?

Data privacy, on the other hand, protects confidential data (usually this is
data about people, but can be data that is sensitive for competitive or
Intellectual Property reasons) while ensuring regulations and policies on the
usage of the data aren't violated. Governance of how the data is used is
enforced using cryptography – ensuring that no user can abuse their access to
data.

Data Privacy solutions come pre-baked with governance policies to ensure that
you cannot accidentally identify an individual from processing their data. This
is what we mean by “Can’t be evil” – it’s mathematically impossible to abuse the
data. The processing of the data can only produce the analytics it’s supposed
to, and nothing else.

These solutions maintain an audit trail how someone’s data was used. They ensure
that malicious attempts to access the data are rejected. They don’t have a
trusted third party in the middle that is able to potentially abuse
administrative permissions. Data protection is also often a side effect of Data
Privacy, but its primary objective is to ensure that Personally Identifiable
Information (PII) and Protected Health Information (PHI) are handled ethically.

Unlike just Data Protection Solutions, Data Privacy solutions ensure that a
virus can’t run with additional protections. While working with AI models, they
ensure that no patient data can be reverse engineered from the model. They
ensure that cross-referencing attacks are infeasible. Really, with ANY usage of
the system, they ensure that regulatory compliance can’t be broken, and no
individual person can ever be identified.

Because of the strong governance enforcements on the data, Data Privacy
solutions can go even further and fuse any arbitrary contract or regulation on
the data. This ensures that two companies collaborating on a project aren’t just
relying on paper contracts with human enforcers to ensure that their data is
safeguarded.

To summarize, Data Privacy solutions ensure that data about people are used
ethically without any likelihood of violations.


WHAT DOES THIS MEAN?

Data protection is about controlling who can see or obtain the data, using
measures like encryption, access controls, and physical security mechanisms.
Data privacy is about how and for what purposes the data can be used once access
is granted.





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RIDDHIMAN DAS

Curious. Gritty. Optimist.
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