Recently, deep learning has been widely used in forensics tools to detect and localize forgery images. However, its susceptibility to adversarial attacks highlights the need for the exploration of anti-forensics research. To achieve this, we introduce an ...

Computation delegation to untrusted third-party while maintaining data confidentiality is possible with homomorphic encryption (HE). However, in many cases, the data was encrypted using another cryptographic scheme such as AES-GCM. Hybrid encryption (...

Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model ...

Keeping open-source software (OSS) up to date is one potential solution to prevent known vulnerabilities. However, it requires frequent and costly testing and may introduce compatibility issues. Consequently, developers often choose to backport security ...

Mixed Boolean Arithmetic (MBA) obfuscation transforms a program expression into an equivalent but complex expression that is hard to understand. MBA obfuscation has been popular to protect programs from reverse engineering thanks to its simplicity and ...

We revisit the concurrent security guarantees of the well-known Anonymous Credentials Light (ACL) scheme (Baldimtsi and Lysyanskaya, CCS'13). This scheme was originally proven secure when executed sequentially, and its concurrent security was left as an ...

Vector Oblivious Linear Evaluation (VOLE) supports fast and scalable interactive Zero-Knowledge (ZK) proofs. Despite recent improvements to VOLE-based ZK, compiling proof statements to a control-flow oblivious form (e.g., a circuit) continues to lead to ...

Given the widespread use of Python and its sustaining impact, the security and reliability of the Python runtime system is highly and broadly critical. Yet with real-world bugs in Python runtimes being continuously and increasingly reported, technique/...

Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using ...

The SPDZ multiparty computation protocol \citeC:DPSZ12 allows n parties to securely compute arithmetic circuits over a finite field, while tolerating up to n-1 active corruptions. A line of work building upon SPDZ has made considerable improvement to the ...

In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove ...

The rise of large language model-based code generation (LLCG) has enabled various commercial services and APIs. Training LLCG models is often expensive and time-consuming, and the training data are often large-scale and even inaccessible to the public. ...

Secure RAM computation allows a number of parties to evaluate a function represented as a random-access machine (RAM) program in a way that reveals nothing about the private inputs of the parties except from what is already revealed by the function ...

Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum ...

Publicly Verifiable Secret Sharing (PVSS) is a fundamental primitive that allows to share a secret S among n parties via a publicly verifiable transcript T. Existing (efficient) PVSS are only proven secure against static adversaries who must choose who ...

Verifying the robustness of machine learning models against evasion attacks at test time is an important research problem. Unfortunately, prior work established that this problem is NP-hard for decision tree ensembles, hence bound to be intractable for ...

The complex optimizations supported by modern compilers allow for compiler provenance recovery at many levels. For instance, it is possible to identify the compiler family and optimization level used when building a binary, as well as the individual ...

SNARK is a well-known family of cryptographic tools that is increasingly used in the field of computation integrity at scale. In this area, multiple works have introduced SNARK-friendly cryptographic primitives: hashing, but also encryption and signature ...

In MPC, we usually represent programs as circuits. This is a poor fit for programs that use complex control flow, as it is costly to compile control flow to circuits. This motivated prior work to emulate CPUs inside MPC. Emulated CPUs can run complex ...

Progress has recently been made on specifying instruction set architectures (ISAs) in executable formalisms rather than through prose. However, to date, those formal specifications are limited to the functional aspects of the ISA and do not cover its ...