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Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different…
Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in…
Memory corruption attacks remain the primary threat for computer security. Information flow tracking or taint analysis has been proven to be effective against most memory corruption attacks. However, there are two shortcomings with current…
Recent advances in secure hardware technologies, such as Intel SGX or ARM TrustZone, offer an opportunity to substantially reduce the costs of Byzantine fault-tolerance by placing the program code and state within a secure enclave known as…
Trusted execution environments (TEEs) offer hardware-assisted means to protect code and data. However, as shown in numerous results over the years, attackers can use side-channels to leak data access patterns and even single-step the code.…
Trusted execution environments like Intel SGX provide \emph{enclaves}, which offer strong security guarantees for applications. Running entire applications inside enclaves is possible, but this approach leads to a large trusted computing…
With the increasing user base of Android devices and advent of technologies such as Internet Banking, delicate user data is prone to be misused by malware and spyware applications. As the app developer community increases, the quality…
Trusted Execution Environments (TEEs) are rapidly emerging as a root-of-trust for protecting sensitive applications and data using hardware-backed isolated worlds of execution. TEEs provide robust assurances regarding critical algorithm…
Edge intelligence enables resource-demanding Deep Neural Network (DNN) inference without transferring original data, addressing concerns about data privacy in consumer Internet of Things (IoT) devices. For privacy-sensitive applications,…
Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current…
Unauthorized data alteration has been a longstanding threat since the emergence of malware. System and application software can be reinstalled and hardware can be replaced, but user data is priceless in many cases. Especially in recent…
Deep Learning (DL) is increasingly used in safety-critical applications, raising concerns about its reliability. DL suffers from a well-known problem of lacking robustness, especially when faced with adversarial perturbations known as…
Eliminating vulnerabilities from low-level code is vital for securing software. Static analysis is a promising approach for discovering vulnerabilities since it can provide developers early feedback on the code they write. But, it presents…
Securing sensitive operations in today's interconnected software landscape is crucial yet challenging. Modern platforms rely on Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, to isolate security sensitive code…
The increasing adoption of Large Language Models (LLMs) in cloud environments raises critical security concerns, particularly regarding model confidentiality and data privacy. Confidential computing, enabled by Trusted Execution…
Using cloud-based applications comes with privacy implications, as the end-user looses control over their data. While encrypting all data on the client is possible, it largely reduces the usefulness of database management systems (DBMS)…
Cryptocurrency introduces usability challenges by requiring users to manage signing keys. Popular signing key management services (e.g., custodial wallets), however, either introduce a trusted party or burden users with managing signing key…
Current marketplaces rely on search mechanisms with distributed systems but centralized governance, making them vulnerable to attacks, failures, censorship and biases. While search mechanisms with more decentralized governance (e.g.,…
Secure aggregation enables a group of mutually distrustful parties, each holding private inputs, to collaboratively compute an aggregate value while preserving the privacy of their individual inputs. However, a major challenge in adopting…
Sensitive computation often has to be performed in a trusted execution environment (TEE), which, in turn, requires tamper-proof hardware. If the computational fabric can be tampered with, we may no longer be able to trust the correctness of…