Related papers: Private Data Objects: an Overview
Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The key challenges for adoption of this technology in financial…
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed…
Machine learning (ML) models have been shown to leak private information from their training datasets. Differential Privacy (DP), typically implemented through the differential private stochastic gradient descent algorithm (DP-SGD), has…
The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises…
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data. However, this approach…
Online trackers personalize ads campaigns, exponentially increasing their efficacy compared to traditional channels. The downside of this is that thousands of mostly unknown systems own our profiles and violate our privacy without our…
The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications. These applications gather contextual information about users and provide some services, such as personalized offers, through…
Third-party analysis on private records is becoming increasingly important due to the widespread data collection for various analysis purposes. However, the data in its original form often contains sensitive information about individuals,…
Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized Online Social Networks (OSNs). Online Social Networks are based on centralized architecture (e.g., Facebook, Twitter, or…
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization…
Despite the hype about blockchains and distributed ledgers, no formal abstraction of these objects has been proposed. To face this issue, in this paper we provide a proper formulation of a distributed ledger object. In brief, we define a…
Differential private (DP) query and response mechanisms have been widely adopted in various applications based on Internet of Things (IoT) to leverage variety of benefits through data analysis. The protection of sensitive information is…
Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local differentially private (LDP) approaches are gaining more…
We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both…
A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function $f$ of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that…
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework…
Enforcing integrity and confidentiality of users' application code and data is a challenging mission that any software developer working on an online production grade service is facing. Since cryptology is not a widely understood subject,…
Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contain private information that requires proper protection.…
Ciphertexts of an order-preserving encryption (OPE) scheme preserve the order of their corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this preserved order. At another end, differential privacy has…
A smart contract on a blockchain cannot keep a secret because its data is replicated on all nodes in a network. To remedy this problem, it has been suggested to combine blockchains with trusted execution environments (TEEs), such as Intel…