Mutual Authentication in RFID: Security and Privacy

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Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

Succinct zero knowledge proofs (i.e. zkSNARKs) are powerful cryptographic tools that enable a prover to convince a verifier that a given statement is true without revealing any additional information. Their attractive privacy properties have led to much ac ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

The Bitcoin architecture heavily relies on the ECDSA signature scheme which is broken by quantum adversaries as the secret key can be computed from the public key in quantum polynomial time. To mitigate this attack, bitcoins can be paid to the hash of a pu ...

Forkciphers are a new kind of primitive proposed recently by Andreeva et al. for efficient encryption and authentication of small messages. They fork the middle state of a cipher and encrypt it twice under two smaller independent permutations. Thus, forkci ...

Mutual Authentication in RFID: Security and Privacy

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Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Enforcing Privacy in Distributed Learning With Performance Guarantees

Scalable and Privacy-Preserving Federated Principal Component Analysis

EOS: Efficient Private Delegation of zkSNARK Provers

Privacy-preserving federated neural network training and inference

On the (In)security of Peer-to-Peer Decentralized Machine Learning

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Making Classical (Threshold) Signatures Post-quantum for Single Use on a Public Ledger

Cryptanalysis of ForkAES