arXiv subjects

Bernardo Ferreira

Publications and source records attributed to Bernardo Ferreira.

3 recordsLinked to original sources

arXiv2026

Detecting Vulnerabilities in Encrypted Software Code while Ensuring Code Privacy

Software vulnerabilities continue to be the primary cause of cyberattacks. It is crucial to identify vulnerabilities in applications' source code before attackers gain access to them and exploit any vulnerability they may contain. Developers have used static analysis tools (SATs) to find vulnerabilities in unprotected application code, and software testing companies have started offering software code analysis as a service to assist developers in these findings. Such services require access to unprotected code, which raises concerns about its privacy and intellectual property theft. Attackers can also perform this analysis using similar tools, if they gain access to the code. It is, therefore, beneficial to have a system that can maintain code privacy by protecting it with cryptographic techniques, while still allowing authorised people to detect vulnerabilities in the encrypted code. This paper presents such a solution, a novel approach to Software Quality and Privacy that allows source code to be analysed in a protected manner, preserving its privacy. The proposed solution combines Static Analysis with Searchable Symmetric Encryption (SSE) for confidential vulnerability detection, enabling data and dependency tracking for data flow analysis over encrypted source code. The solution represents the code's data and control flows as an Encrypted Inverted Index, in a connected way that enables SSE's queries for vulnerability discovery. The solution was implemented as the CoCoA tool and evaluated with synthetic and real PHP web applications. Results show that CoCoA has similar precision as (non-confidential) SATs - 93% - with real applications, requiring only 209 ms to process 4k LoC - a modest overhead of 42.7% compared to a non-confidential baseline. This paper also defines a new research field - Confidential Code Analysis -, from which other types of code analysis tasks can be derived.

cs.SE↗

arXiv2025

MVP-ORAM: a Wait-free Concurrent ORAM for Confidential BFT Storage

It is well known that encryption alone is not enough to protect data privacy. Access patterns, revealed when operations are performed, can also be leveraged in inference attacks. Oblivious RAM (ORAM) hides access patterns by making client requests oblivious. However, existing protocols are still limited in supporting concurrent clients and Byzantine fault tolerance (BFT). We present MVP-ORAM, the first wait-free ORAM protocol that supports concurrent fail-prone clients. In contrast to previous works, MVP-ORAM avoids using trusted proxies, which require additional security assumptions, and concurrency control mechanisms based on inter-client communication or distributed locks, which limit overall throughput and the capability of tolerating faulty clients. Instead, MVP-ORAM enables clients to perform concurrent requests and merge conflicting updates as they happen, satisfying wait-freedom, i.e., clients make progress independently of the performance or failures of other clients. Since wait and collision freedom are fundamentally contradictory goals that cannot be achieved simultaneously in an asynchronous concurrent ORAM service, we define a weaker notion of obliviousness that depends on the application workload and number of concurrent clients, and prove MVP-ORAM is secure in practical scenarios where clients perform skewed block accesses. By being wait-free, MVP-ORAM can be seamlessly integrated into existing confidential BFT data stores, creating the first BFT ORAM construction. We implement MVP-ORAM on top of a confidential BFT data store and show our prototype can process hundreds of 4KB accesses per second in modern clouds.

cs.CR↗

arXiv2015

Privacy-Preserving Content-Based Image Retrieval in the Cloud

Storage requirements for visual data have been increasing in recent years, following the emergence of many new highly interactive, multimedia services and applications for both personal and corporate use. This has been a key driving factor for the adoption of cloud-based data outsourcing solutions. However, outsourcing data storage to the Cloud also leads to new challenges that must be carefully addressed, especially regarding privacy. In this paper we propose a secure framework for outsourced privacy-preserving storage and retrieval in large image repositories. Our proposal is based on a novel cryptographic scheme, named IES-CBIR, specifically designed for media image data. Our solution enables both encrypted storage and querying using Content Based Image Retrieval (CBIR) while preserving privacy. We have built a prototype of the proposed framework, formally analyzed and proven its security properties, and experimentally evaluated its performance and precision. Our results show that IES-CBIR is provably secure, allows more efficient operations than existing proposals, both in terms of time and space complexity, and enables more realistic, interesting and practical application scenarios.

cs.CR↗