Recent research in tabular data synthesis has focused on single tables, whereas real-world applications often involve complex data with tens or hundreds of interconnected tables. Previous approaches to synthesizing multi-relational (multi-table) data fall short in two key aspects: scalability for larger datasets and capturing long-range dependencies, such as correlations between attributes spread across different tables. Inspired by the success of diffusion models in tabular data modeling, we introduce \textbf{C}luster \textbf{La}tent \textbf{Va}riable guided \textbf{D}enoising \textbf{D}iffusion \textbf{P}robabilistic \textbf{M}odels (ClavaDDPM). This novel approach leverages clustering labels as intermediaries to model relationships between tables, specifically focusing on foreign key constraints. ClavaDDPM leverages the robust generation capabilities of diffusion models while incorporating efficient algorithms to propagate the learned latent variables across tables. This enables ClavaDDPM to capture long-range dependencies effectively. Extensive evaluations on multi-table datasets of varying sizes show that ClavaDDPM significantly outperforms existing methods for these long-range dependencies while remaining competitive on utility metrics for single-table data.

Encryption provides a method to protect data out-sourced to a DBMS provider, e.g., in the cloud. However, performing database operations over encrypted data requires specialized encryption schemes that carefully balance security and performance. In this paper, we present a new encryption scheme that can efficiently perform equi-joins over encrypted data using only software and a single server with better security than the state-of-the-art. In particular, our encryption scheme reduces the leakage to equality of rows that match a selection criterion and only reveals the transitive closure of the union of the leakages of each query in a series of queries. Our encryption scheme is provable secure. We implemented our encryption scheme and evaluated it over a dataset from the TPC- H benchmark.

Watermarking algorithms have been introduced in the past years to protect deep learning models against unauthorized re-distribution. We investigate the robustness and reliability of state-of-the-art deep neural network watermarking schemes. We focus on backdoor-based watermarking and propose two simple yet effective attacks -- a black-box and a white-box -- that remove these watermarks without any labeled data from the ground truth. Our black-box attack steals the model and removes the watermark with only API access to the labels. Our white-box attack proposes an efficient watermark removal when the parameters of the marked model are accessible, and improves the time to steal a model up to twenty times over the time to train a model from scratch. We conclude that these watermarking algorithms are insufficient to defend against redistribution by a motivated attacker.

Outlier detection plays a significant role in various real world applications such as intrusion, malfunction, and fraud detection. Traditionally, outlier detection techniques are applied to find outliers in the context of the whole dataset. However, this practice neglects the data points, namely contextual outliers, that are not outliers in the whole dataset but in some specific neighborhoods. Contextual outliers are particularly important in data exploration and targeted anomaly explanation and diagnosis. In these scenarios, the data owner computes the following information: i) The attributes that contribute to the abnormality of an outlier (metric), ii) Contextual description of the outlier's neighborhoods (context), and iii) The utility score of the context, e.g. its strength in showing the outlier's significance, or in relation to a particular explanation for the outlier. However, revealing the outlier's context leaks information about the other individuals in the population as well, violating their privacy. We address the issue of population privacy violations in this paper. There are two main challenges in defining and applying privacy in contextual outlier release. In this setting, the data owner is required to release a valid context for the queried record, i.e. a context in which the record is an outlier. Hence, the first major challenge is that the privacy technique must preserve the validity of the context for each record. We propose techniques to protect the privacy of individuals through a relaxed notion of differential privacy to satisfy this requirement. The second major challenge is applying the proposed techniques efficiently, as they impose intensive computation to the base algorithm. To overcome this challenge, we propose a graph structure to map the contexts to, and introduce differentially private graph search algorithms as efficient solutions for the computation problem caused by differential privacy techniques.

Over-Threshold Multi-Party Private Set Intersection (OT-MP-PSI) is the problem where several parties, each holding a set of elements, want to know which elements appear in at least t sets, for a certain threshold t, without revealing any information about elements that do not meet this threshold. This problem has many practical applications, but current solutions require a number of expensive operations exponential in t and thus are impractical.

Private set intersection (PSI) allows two parties to compute the intersection of their data without revealing the data they possess that is outside of the intersection. However, in many cases of joint data analysis, the intersection is also sensitive. We define differentially private set intersection and we propose new protocols using (leveled) homomorphic encryption where the result is differentially private. Our circuit-based approach has an adaptability that allows us to achieve differential privacy, as well as to compute predicates over the intersection such as cardinality. Furthermore, our protocol produces differentially private output for set intersection and set intersection cardinality that is optimal in terms of communication and computation complexity. For a client set of size$m$and a server set of size$n$, where$m$is smaller than$n$, our communication complexity is$O(m)$while previous circuit-based protocols only achieve$O(n+m)$communication complexity. In addition to our asymptotic optimizations which include new analysis for using nested cuckoo hashing for PSI, we demonstrate the practicality of our protocol through an implementation that shows the feasibility of computing the differentially private intersection for large data sets containing millions of elements.

Today's commonly used digital signatures will not be secure if a quantum computer exists. One time signatures (OTS) base security on the one way property of hash functions and will stay secure against an adversary with access to a quantum computer. These schemes however suffer from large public and private keys, as well as large signature size. We propose an OTS that uses Bloom filters to enhance the efficiency without sacrificing security, and show the required sizes of public/private keys, as well as the signature size will all reduce for the same security level.