What steps can you take to secure your neural network models and protect users' data?

Here you need to clarify that not all techniques are applicable to encrypted data. One of the promising approaches Homomorphic encryption allows computations to be performed on encrypted data without decrypting it. With homomorphic encryption, data remains encrypted during model training or inference, ensuring that sensitive information is never exposed in its raw form.

To secure neural network models and protect user data, it's essential to employ multiple strategies. Firstly, encryption is crucial for safeguarding data at rest and during transit. Differential privacy can add noise to the data to protect individual user privacy. Federated learning allows for decentralized model training, thereby avoiding the need to transfer sensitive data to a central server. For securing model inference, techniques like homomorphic encryption, secure multi-party computation, or trusted execution environments can be used to protect against various types of attacks. Monitoring model performance metrics and real-world behavior is critical for timely detection and response to anomalies or cybersecurity threats.

While encrypting your data during processing is becoming standard practice, homomorphic encryption is usually too costly for most common usecases. This gap is filled by other encryption in-use technologies such as Confidential Computing and Secure Multiparty Computation that use different ways to encrypt the data while processing most often with lower computational overhead. For Confidential Computing that can be as low as 5%, while with Fully Homomorphic Encryption it is 3-4x vs unencrypted processing.

As said the best techniques are Data Encryption, Access Control, Secure Model Deployment, Regular Updates and Patching, Input Validation, Monitoring and Anomaly Detection, Federated Learning, Privacy-Preserving Techniques, Third-party Audits, Secure Data Storage, User Consent and Transparency, Incident Response Plan, Regulatory Compliance,

Differential privacy is best described by Cynthia Dwork in " The Algorithmic Foundations of Differential Privacy, 2014". As she notes, differential privacy describes a promise, made by a data curator to a data subject and goes like this: “you will not be affected, adversely or otherwise, by allowing your data to be used in any study, no matter what other studies, data sets, or information from other sources is available”.

While Differential Privacy is a robust statistical way of protecting users data when analyzing it ; it is also quite impractical for most usecases as it requires knowing from the start all the computations that will be performed on that data. In addition, the notion of "ε" is crucial in calculating privacy boundaries and its not an intuitive metric to be understood by most non-technical people that need to take a risk decision. Instead a mixture of different techniques is usually used along with a "human-in-the-loop" that verifies that results are as expected. As a buyer beware of solutions selling Differential Privacy out of the box.

Federated Learning builds on a large corpora of existing research in the field of Secure Multiparty Computation (SMC). SMC allows a number of mutually distrustful parties to carry out a joint computation of a function of their inputs, while preserving the privacy of the inputs. The two main SMC primitives are: Homomorphic Encryption and Secret Sharing. Homomorphic encryption schemes allow arithmetic operations to be performed locally on the plaintext values, based on their encrypted values. In secret sharing schemes on the other hand, while addition can be performed locally by an addition of the local (plaintext) shares, multiplication requires distributed collaboration among the parties.

Federated Learning can easily become much more costly if it doesn't fit your use-case. If you don't have the required hardware at the edge, if your data is too small, if the datasets are not perfectly homogeneous, if there are outlier datasets or heavily imbalanced datasets, if your data needs preprocessing, Federated Learning won't be easy to implement.

One way to secure neural network models is through model watermarking. Model watermarking embeds a unique identifier or pattern into a neural network, acting as a digital signature. This ensures that the model's ownership and origin can be verified. If an unauthorized party replicates or uses the model, the watermark can be detected, confirming potential intellectual property theft or misuse. This technique enhances model security and provides a means of attribution to its rightful owner.

Determining what constitutes good and bad model performance necessitates the establishment of realistic expectations. It's improbable that the model will maintain the same level of performance as during its initial evaluation on the training data used for its design. The data environment is subject to change over time, and a certain degree of performance degradation is anticipated. Consequently, it is essential to define both lower and upper thresholds for acceptable performance. The choice of these thresholds is contingent on the specific application at hand and should involve input from not just machine learning experts but also domain experts familiar with the underlying context.

Consider the General Data Protection Regulation (GDPR) implemented by the European Union. Under GDPR, companies are required to obtain explicit and informed consent from users before collecting or processing their personal data. Users have the right to know what data is being collected, how it will be used, and with whom it will be shared. Additionally, users have the right to withdraw their consent at any time and have their data deleted. This regulation has brought greater transparency and control over personal data for users, ensuring that their privacy rights are respected.

Another approach to consider is Multi-party computation (MPC). MPC is a technique that enables multiple parties to collaboratively compute functions on their private data while keeping the data itself confidential. In the context of neural network models, MPC allows for model training using data from different sources without exposing individual user data. This approach ensures user data privacy by only revealing the computation results (e.g., model parameters) to the parties involved, making it a valuable tool when data sharing is necessary, but data security is a top priority.

Create a threat model. Who are you protecting against and when. There is no magic bullet when it comes to security and privacy and most successful real-world deployments are those that protect against realistic attacks from realistic actors. Industry leading vendors should be able to thoroughly discuss with you the different tradeoffs of various techniques and help you choose the technology that suits your threat model.