Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML).

The popularity of cloud computing has made outsourced databases prevalent in real-world applications. To protect data security, numerous encrypted outsourced databases have been proposed for this paradigm. However, the maintenance of encrypted databases (EDBs) has scarcely been addressed. In this paper, we focus on a typical maintenance task - functional dependency (FD) discovery. We develop novel FD protocols in EDBs while guaranteeing minimal leakages: nothing is revealed besides the database size and the actual discovered FDs. As far as we know, we are the first to formally define secure FD discovery with minimal leakage. We present two oblivious FD discovery protocols and prove them secure in the presence of the persistent adversary (monitoring processes on the server). The first protocol leverages oblivious RAM (ORAM) and is suitable for dynamic databases. The second protocol relies on oblivious sorting and is more practical in static databases due to high parallelism. We present a thorough experimental evaluation of the proposed methods.

Oblivious map (OMAP) is an important component in encrypted databases, utilized to prevent the server inferring sensitive information about client's encrypted databases based on access patterns. Despite its widespread usage and importance, existing OMAP solutions face practical challenges, including the need for a large number of interaction rounds between the client and server, as well as substantial communication bandwidth. For example, the SOTA protocol OMIX++ in VLDB 2024 still requires O (log n ) interaction rounds and O (log 2 n ) communication bandwidth per access, where n denotes the total number of key-value pairs stored. In this work, we introduce more practical and efficient OMAP constructions. Consistent with all prior OMAPs, our constructions also adapt only the tree-based Oblivious RAM (ORAM) and oblivious data structures (ODS) to achieve OMAP for enhanced practicality. In complexity, our approach needs O (log n /log log n )+ O (log λ ) interaction rounds and O (log 2 n /log log n ) + O (log λ log n ) communication bandwidth per data access where λ is the security parameter. This new complexity results from our two main contributions. First, unlike prior works relying solely on search trees , we design a novel framework for OMAP that combines hash table with search trees. Second, we propose a more efficient tree-based ORAM named DAORAM, which is of significant independent interest. This new ORAM accelerates our constructions as it supports obliviously accessing hash tables more efficiently. We implement both our proposed constructions and prior methods to experimentally demonstrate that our constructions substantially outperform prior methods in terms of efficiency.

The current challenge of Deepfake detection is the cross-domain performance on unseen Deepfake data. Instead of extracting forgery artifacts that are robust to the cross-domain scenarios as most previous works, we propose a novel method named Single-sIde Guided Meta-leArning framework for DeepFake detection (SIGMA-DF) which simulates the cross-domain scenarios during training by synthesizing virtual testing domain through meta-learning. In addition, SIGMA-DF integrates the meta-learning algorithm with a new ensemble meta-learning framework, which separately trains multiple meta-learners in the meta-train phase to aggregate multiple domain shifts in each iteration. Hence multiple cross-domain scenarios are simulated, better leveraging the domain knowledge. In addition, considering the contribution of hard samples in single-side distribution optimization, a novel weighted single-side loss function is proposed to only narrow the intra-class distance between real faces and enlarge the inter-class distance for both real and fake faces in embedding space with the awareness of sample weights. Extensive experiments are conducted on several standard Deepfake detection datasets to demonstrate that the proposed SIGMA-DF achieves state-of-the-art performance. In particular, in the cross-domain evaluation from FF++ to Celeb-DF and DFDC, our SIGMA-DF outperforms the baselines by 4.4% and 4.5% in terms of AUC, respectively.

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.

Encrypted database is an innovative technology proposed to solve the data confidentiality issue in cloud-based DB systems. It allows a data owner to encrypt its database before uploading it to the service provider; and it allows the service provider to execute SQL queries over the encrypted data. Most of existing encrypted databases (e.g., CryptDB in SOSP '11) do not support data interoperability: unable to process complex queries that require piping the output of one operation to another. To the best of our knowledge, SDB (SIGMOD '14) is the only encrypted database that achieves data interoperability. Unfortunately, we found SDB is not secure! In this paper, we revisit the security of SDB and propose a ciphertext-only attack named co-prime attack. It successfully attacks the common operations supported by SDB, including addition, comparison, sum, equi-join and group-by. We evaluate our attack in three real-world benchmarks. For columns that support addition and comparison , we recover 84.9% -- 99.9% plaintexts. For columns that support sum, equi-join and group-by , we recover 100% plaintexts. Besides, we provide potential countermeasures that can prevent the attacks against sum, equi-join, group-by and addition. It is still an open problem to prevent the attack against comparison.