Biblio

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

We initiate an effort to provide a rigorous, holistic and modular security analysis of OpenStack. OpenStack is the prevalent open-source, non-proprietary package for managing cloud services and data centers. It is highly complex and consists of multiple inter-related components which are developed by separate, loosely coordinated groups. All of these properties make the security analysis of OpenStack both a worthy mission and a challenging one. We base our modeling and security analysis in the universally composable (UC) security framework. This allows specifying and proving security in a modular way – a crucial feature when analyzing systems of such magnitude. Our analysis has the following key features: 1) It is user-centric: It stresses the security guarantees given to users of the system in terms of privacy, correctness, and timeliness of the services. 2) It considers the security of OpenStack even when some of the components are compromised. This departs from the traditional design approach of OpenStack, which assumes that all services are fully trusted. 3) It is modular: It formulates security properties for individual components and uses them to prove security properties of the overall system. Specifically, this work concentrates on the high-level structure of OpenStack, leaving the further formalization and more detailed analysis of specific OpenStack services to future work. Specifically, we formulate ideal functionalities that correspond to some of the core OpenStack modules, and then proves security of the overall OpenStack protocol given the ideal components. As demonstrated within, the main challenge in the high-level design is to provide adequately fine-grained scoping of permissions to access dynamically changing system resources. We demonstrate security issues with current mechanisms in case of failure of some components, propose alternative mechanisms, and rigorously prove adequacy of then new mechanisms within our modeling.

Clone product injection into supply chains causes serious problems for industry and customers. Many mechanisms have been introduced to detect clone products in supply chains which make use of RFID technologies. This article gives an overview of these mechanisms, categorizes them by hardware change requirements, and compares their attributes.

We introduce a Markov-model-based framework for Moving Target Defense (MTD) analysis. The framework allows modeling of a broad range of MTD strategies, provides general theorems about how the probability of a successful adversary defeating an MTD strategy is related to the amount of time/cost spent by the adversary, and shows how a multilevel composition of MTD strategies can be analyzed by a straightforward combination of the analysis for each one of these strategies. Within the proposed framework we define the concept of security capacity which measures the strength or effectiveness of an MTD strategy: the security capacity depends on MTD specific parameters and more general system parameters. We apply our framework to two concrete MTD strategies.