Privacy is a fundamental human right and a growing concern for many users of software systems. Algorithms are the core components of software design, and they can have significant impacts on how data is collected, processed, stored, and shared. How can you design algorithms that respect user privacy and avoid potential harms? Here are some best practices to consider.
Before you design an algorithm, you should have a clear and specific purpose for why you need to use data and what you want to achieve with it. You should also identify the stakeholders who will be affected by your algorithm and their expectations and rights. This will help you to align your algorithm with your ethical principles and legal obligations, and to avoid unnecessary or excessive data collection and processing.
1) Ιmplement strong security measures for data storage, including encryption and access controls. Restrict access to only authorized personnel. 2) Implement differential privacy techniques to add noise or randomness to query results to protect individual user data while still providing useful aggregated statistics.
One of the key principles of privacy by design is data minimization. This means that you should only collect and use the data that is relevant and necessary for your purpose, and delete or anonymize it when it is no longer needed. You should also avoid storing or transferring data in plain text, and use encryption or hashing techniques to protect it from unauthorized access or leakage. Data minimization can reduce the risks of data breaches, identity theft, or discrimination.
Most of the things regarding privacy have to do with data. The first thing to consider is user consent and transparency. Inform users about data collection and processing and obtain their explicit consent. The process should be clear, transparent, and easily understandable. Give them the option of opting out if necessary. Additionally we should consider about regional and global privacy regulations, such as the GDPR (General Data Protection Regulation) in Europe, and design algorithms to be compliant, by ensuring that teams are aware of privacy concerns and stay updated with the latest techniques.
Consider encoding your data during collection. Codify and classify answers and just store a category identifier (just a number or a letter) rather than the complete answer. If you don't plan on going back to the user, anonymise it. It you do need to be able to get back to them separate the user identification data from the data you're working with and just use a GUID to link them. For anyone without access to the user identification side if the db, the data is, essentially, anonymised.
Another important aspect of privacy by design is performance optimization. This means that you should ensure that your algorithm is accurate, reliable, efficient, and fair. You should also test and validate your algorithm regularly, and monitor and audit its outcomes and impacts. Performance optimization can enhance the quality and trustworthiness of your algorithm, and prevent or mitigate errors, biases, or harms.
What? Performance optimisation is about making it run fast. It shouldn't affect any other aspects of the design, although transparency and explainability are often sacrificed in the name of performance. Get it working properly before you start worrying about how fast it is. Don't try writing cleaver/fast code. You audience is an optimising compiler, not the hardware it's running on. Confuse the compiler and all your other attributes can go out of the window.
A crucial element of privacy by design is user transparency and consent. This means that you should inform your users about what data you collect, how you use it, who you share it with, and what rights and options they have. You should also obtain their explicit and informed consent before you process their data, and respect their preferences and choices. User transparency and consent can foster user awareness and empowerment, and increase user satisfaction and loyalty.
How is this a part of a data processing algorithm? It might be part of the deployment of an application, but it does nothing about the storage or processing of the data. You certainly can't write it in pseudo-code. Frankly I'd be more interested in the steps being taken to protect it from misuse and theft
A final tip for privacy by design is collaboration and communication. This means that you should involve and consult with other experts, such as privacy officers, legal advisors, ethicists, or user representatives, who can provide valuable insights and feedback on your algorithm design. You should also communicate and explain your algorithm to your users, clients, or partners, and address any questions or concerns they may have. Collaboration and communication can improve your algorithm's accountability and legitimacy, and build trust and confidence.
Designing algorithms that respect user privacy is not only a technical challenge, but also a moral and social responsibility. By following these best practices, you can create algorithms that are more secure, ethical, and beneficial for your users and society.
Probably the best solution is just not to store User's data. The second best solution is setting up your server's location in a country that respects the user's privacy. Only after the infrastructure is secure - we can talk about secure code. Perform threat modelling when the initial solution is ready and before going to production. Set zero trust policy between services. Always consider data to be compromised. Inside the service try to use memory-friendly algorithms as RAM nowadays seem to be expensive for cloud solutions. I suggest requiring 2FA by key as an auth method. This may make your application somewhat secure but still, this is not a silver bullet. Each app has its own specifics and obstacles that are to be solved.
Where's the bit on data encryption at various stages in its life cycle, the selection of appropriate encryption algorithms and key strengths, the requirement to use a quantum resistant encryption algorithm and the requirement for regular key rotation? Where's the bit of a distributed deployment that will respect regional and national laws concerning data movement and processing? Where is the bit on setting appropriate user access permissions and separating the data, coding and privacy teams? Data and coding folks should not be able to access plsintext data in a highly private application. Where's the bit on air gapping? This is a requirement under US HIPPA legislation.