Efficient and privacy-preserving outsourced calculation of rational numbers

Singapore Ministry of Education Academic Research Fund Tier

Privacy-preserving outsourced calculation toolkit in the cloud

tru

Lightning-fast and privacy-preserving outsourced computation in the cloud

AXA Research Fun

An efficient privacy-preserving outsourced calculation toolkit with multiple keys

Singapore National Research Foundatio

Towards Validating Risk Indicators Based on Measurement Theory (Extended version)

Due to the lack of quantitative information and for cost-efficiency, most risk assessment methods use partially ordered values (e.g. high, medium, low) as risk indicators. In practice it is common to validate risk indicators by asking stakeholders whether they make sense. This way of validation is subjective, thus error prone. If the metrics are wrong (not meaningful), then they may lead system owners to distribute security investments inefficiently. For instance, in an extended enterprise this may mean over investing in service level agreements or obtaining a contract that provides a lower security level than the system requires. Therefore, when validating risk assessment methods it is important to validate the meaningfulness of the risk indicators that they use. In this paper we investigate how to validate the meaningfulness of risk indicators based on measurement theory. Furthermore, to analyze the applicability of the measurement theory to risk indicators, we analyze the indicators used by a risk assessment method specially developed for assessing confidentiality risks in networks of organizations

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Privacy-preserving outsourced support vector machine design for secure drug discovery

AXA Research Fund, Singapore Management Universit