Federated learning (FL) requires frequent uploading and updating of model parameters, which is naturally vulnerable to gradient leakage attacks (GLAs) that reconstruct private training data through gradients. Although some works incorporate differential privacy (DP) into FL to mitigate such privacy issues, their performance is not satisfactory since they did not notice that GLA incurs heterogeneous risks of privacy leakage (RoPL) with respect to gradients from different communication rounds and clients. In this paper, we propose an Adaptive Privacy-Preserving Federated Learning (Adp-PPFL) framework to achieve satisfactory privacy protection against GLA, while ensuring good performance in terms of model accuracy and convergence speed. Specifically, a leakage risk-aware privacy decomposition mechanism is proposed to provide adaptive privacy protection to different communication rounds and clients by dynamically allocating the privacy budget according to the quantified RoPL. In particular, we exploratively design a round-level and a client-level RoPL quantification method to measure the possible risks of GLA breaking privacy from gradients in different communication rounds and clients respectively, which only employ the limited information in general FL settings. Furthermore, to improve the FL model training performance (i.e., convergence speed and global model accuracy), we propose an adaptive privacy-preserving local training mechanism that dynamically clips the gradients and decays the noises added to the clipped gradients during the local training process. Extensive experiments show that our framework outperforms the existing differentially private FL schemes on model accuracy, convergence, and attack resistance.

Order-preserving encryption (OPE) allows efficient comparison operations over encrypted data and thus is popular in encrypted databases. However, most existing OPE schemes are vulnerable to inference attacks as they leak plaintext frequency. To this end, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed and claim to prevent the leakage of frequency. FH-OPE schemes are considered an important step towards mitigating inference attacks. Unfortunately, there are still vulnerabilities in all existing FH-OPE schemes. In this work, we revisit the security of all existing FH-OPE schemes. We are the first to demonstrate that plaintext frequency hidden by them is recoverable. We present three ciphertext-only attacks named frequency-revealing attacks to recover plaintext frequency. We evaluate our attacks in three real-world datasets. They recover over 90% of plaintext frequency hidden by any existing FH-OPE scheme. With frequency revealed, we also show the potentiality to apply inference attacks on existing FH-OPE schemes. Our findings highlight the limitations of current FH-OPE schemes. Our attacks demonstrate that achieving frequency-hiding requires addressing the leakages of both non-uniform ciphertext distribution and insertion orders of ciphertexts, even though the leakage of insertion orders is always ignored in OPE.