What should be the fundamentals of Privacy?

This is a complex topic, and I'm aware that my understanding is not complete. There are many perspectives and factors involved. A lot of work has been done and is being done today as well to address the fundamentals of Privacy. I am writing here with the ignorance of my limited knowledge. The thoughts are very restricted to our current context of HMI and ambient systems.  

Importance of data in the age of HMI, or any further technical advancement.

I would like to answer this by bringing attention to how we operate as humans. The way we think and act is shaped by our evolutionary past, and much of our behavior is actually driven by subconscious, automated processes. This is reflected in everything from our basic survival instincts to the habits and patterns that we follow without thinking about them. All of this information is encoded in our genes. Genes are a very sophisticated way of data storage which we still don't understand. An important point for us to note is that we and our current level of intelligence are built over the data accumulated in our genes over millions of years.

Similarly for any other machine intelligence to exist, we need it to feed with data, context, and memory. In the current day, to reach a level of human-machine integration as envisaged in previous notes, we would need a lot of data to give to these machines. Not just the digital data but physical data as well. However, it is crucial that we carefully regulate and control access to this data to prevent misuse. The implications of the collected data are profound, and we must approach it with caution.

Why we can’t keep the data models of the past going on in the next era?

In this section we will try to answer, why the data collection and utilization models of past two decades cannot stay for the next two decades.

In the 2000s and 2010s, the technical products developed were software. The primary purpose of these products was to serve the limited objective, invoicing, or messaging, or email or similar. The data collected by serving these services was a by-product, never the core purpose. By now, we have built tools for almost everything imaginable, from social media for pets, to secure instant street-side payments.

From here, the only relevance in software solutions is coming from utilizing the data collected in any form. My argument is that for future innovations just relying on the data collected via these utility software won’t be suitable. Unsuitable in two ways, one is the data collection and usage was never their primary task so they were overtime modified to feed this purpose, thus they lack the fundamental design to serve the privacy. Second, is the utilization of this data, it should be done keeping in mind the user, their need, and keeping them at the center of everything. Not the other way around.  

Foundational Principles

This section is again highly influenced by my limited knowledge of the field, and it is highly made to serve the segments of HMI, and ambient systems. I will try to put out what foundational principles of privacy should look like:

• Ownership of the data - The ownership and access to data is a fundamental issue. It's important to ensure that humans retain ownership of their data and have full control over how it's used. Without this, the power dynamic between humans and machines could become dangerously imbalanced. Humans should always be in control of the data they generate, and it should never be used in ways that go against their interests or violate their privacy. This principle is vital for protecting individual rights and freedoms in the age of machine intelligence.

• Storage of the data - It's important to consider the way data is stored and accessed by systems. One option is to use edge computing, which keeps data on local devices rather than sending it to a central server. This minimizes the amount of data that needs to be stored in the long term. Additionally, 5G and 6G technologies offer new possibilities for processing data at the edge, allowing systems to work more efficiently and securely. This could have significant implications for individual privacy and security. It gives immense control of data to individuals.

• Transparency of generation & usage of data - Individuals should have a clear understanding of how their data is being used by institutions. They should have the right to know what kind of data is being collected, how it's being used, and who has access to it. This transparency is essential for giving individuals agency and control over their data, and for holding companies accountable for their use of data.

• Clear accountability in case of breaches - When data breaches occur, individuals can suffer serious psychological and economical consequences. It's vital that institutions take responsibility for protecting user data and that they are held accountable when breaches occur. This can involve financial penalties and other legal consequences.

• Security over surveillance - It's vital that we prioritize security, rather than surveillance. While there are legitimate reasons for some forms of surveillance, a system of total surveillance is unacceptable. The government should not have the right to record all Internet activity without authorization. Instead of creating backdoors to allow for surveillance, we should focus on creating security systems that protect data while still respecting the privacy of individuals. This will require a careful balance of interests, but it's the only way to ensure that everyone's rights are respected.

*we will keep reframing this section over time.

Different models

Privacy means different things to different people. To some, privacy is simply the protection of personal information. To others, it encompasses a wide range of values, including autonomy, dignity, and freedom. There is no single, universal definition of privacy, and it can be difficult to reconcile the different perspectives on the subject. However, it's important to understand these differing perspectives in order to develop privacy-enhancing technologies that are truly useful and beneficial to society.

Researchers and policymakers have proposed a variety of models for privacy-enhancing technologies. Some of these models are theoretical in nature, while others have been implemented in practice. No single model is perfect, and each has its own advantages and disadvantages. We will discuss some of the Privacy Enabling Technologies (PETs) below.

1. Multi Party Computational Protocol- Multi-party computation (MPC) is a cryptographic technique that allows multiple parties to securely compute a function without revealing their private inputs. In other words, each party can contribute their data to the computation without revealing that data to the other parties.
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2. Homomorphic Encryption - Homomorphic encryption is a type of encryption that allows you to perform calculations on encrypted data without having to decrypt it first. The output of the calculations remains encrypted, and when decrypted, it is the same as if the calculations had been performed on unencrypted data. This is useful for secure computing, where you want to keep data private while still being able to perform computations on it.
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3. Zero Knowledge Proof - Zero-knowledge proofs (ZKPs) are a type of cryptographic technique that allows one party to prove that they have knowledge of a certain piece of information, without revealing that information to the other party. ZKPs have a wide range of applications in the fields of cryptography, security, and privacy. In blockchain and cryptocurrency applications, ZKPs can be used to verify transactions without revealing the identities of the parties involved, or to prove that a transaction is valid without revealing the details of the transaction.
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4. Differential Privacy - Differential privacy is a mathematical definition of privacy that was developed by computer scientists. In short, differential privacy means that the output of a system is very similar whether a specific individual's data is included or not. This makes it impossible to tell whether a specific individual's data was included in the system or not. Differential privacy is an important concept for privacy-preserving systems, as it helps to ensure that individuals' data is not identifiable or traceable.
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5. Privacy by design - Privacy-enhancing technologies (PETs) are a valuable tool for protecting user privacy, but they can be complex and require some technical knowledge to use. Privacy by design is an alternative approach that embeds privacy considerations into the design of a system from the start. This ensures that user privacy is protected without the need for users to understand or configure any PETs.

The privacy vs. convenience battle

Privacy battle is to be fought keeping in mind many varied factors including the current ubiquity of data-driven ads, and the corresponding economy of 100s of billions of dollars. We need to gauge our way of keeping room for business innovation but also not let go the data privacy in the process. The convenience of free services is important but not at the cost of the possibility of behavioral control. We need to keep in mind the state security is crucial but total surveillance is not. The ongoing battle over privacy is multifaceted and complex.