Privacy-Preserving Decision Tree Training and Prediction against Malicious Server

Privacy-preserving machine learning enables secure outsourcing of machine learning tasks to an untrusted service provider (server) while preserving the privacy of the user\u27s data (client). Attaining good concrete efficiency for complicated machine learning tasks, such as training decision trees, is one of the challenges in this area. Prior works on privacy-preserving decision trees required the parties to have comparable computational resources, and instructed the client to perform computation proportional to the complexity of the entire task. In this work we present new protocols for privacy-preserving decision trees, for both training and prediction, achieving the following desirable properties: 1. Efficiency: the client\u27s complexity is independent of the training-set size during training, and of the tree size during prediction. 2. Security: privacy holds against malicious servers. 3. Practical usability: high accuracy, fast prediction, and feasible training demonstrated on standard UCI datasets, encrypted with fully homomorphic encryption. To the best of our knowledge, our protocols are the first to offer all these properties simultaneously. The core of our work consists of two technical contributions. First, a new low-degree polynomial approximation for functions, leading to faster protocols for training and prediction on encrypted data. Second, a design of an easy-to-use mechanism for proving privacy against malicious adversaries that is suitable for a wide family of protocols, and in particular, our protocols; this mechanism could be of independent interest

Privacy-Preserving intrusion detection over network data

Effective protection against cyber-attacks requires constant monitoring and analysis of system data such as log files and network packets in an IT infrastructure, which may contain sensitive information. To this end, security operation centers (SOC) are established to detect, analyze, and respond to cyber-security incidents. Security officers at SOC are not necessarily trusted with handling the content of the sensitive and private information, especially in case when SOC services are outsourced as maintaining in-house expertise and capability in cyber-security is expensive. Therefore, an end-to-end security solution is needed for the system data. SOC often utilizes detection models either for known types of attacks or for an anomaly and applies them to the collected data to detect cyber-security incidents. The models are usually constructed from historical data that contains records pertaining to attacks and normal functioning of the IT infrastructure under monitoring; e.g., using machine learning techniques. SOC is also motivated to keep its models confidential for three reasons: i) to capitalize on the models that are its propriety expertise, ii) to protect its detection strategies against adversarial machine learning, in which intelligent and adaptive adversaries carefully manipulate their attack strategy to avoid detection, and iii) the model might have been trained on sensitive information, whereby revealing the model can violate certain laws and regulations. Therefore, detection models are also private. In this dissertation, we propose a scenario in which privacy of both system data and detection models is protected and information leakage is either prevented altogether or quantifiably decreased. Our main approach is to provide an end-to-end encryption for system data and detection models utilizing lattice-based cryptography that allows homomorphic operations over the encrypted data. Assuming that the detection models are previously obtained from training data by SOC, we apply the models to system data homomorphically, whereby the model is encrypted. We take advantage of three different machine learning algorithms to extract intrusion models by training historical data. Using different data sets (two recent data sets, and one outdated but widely used in the intrusion detection literature), the performance of each algorithm is evaluated via the following metrics: i) the time that takes to extract the rules, ii) the time that takes to apply the rules on data homomorphically, iii) the accuracy of the rules in detecting intrusions, and iv) the number of rules. Our experiments demonstrates that the proposed privacy-preserving intrusion detection system (IDS) is feasible in terms of execution times and reliable in terms of accurac

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.Comment: Accepted for publication in edited book titled "Federated and Transfer Learning", Springer, Cha