Authenticating Query Results in Edge Computing

10.1109/ICDE.2004.1320027Proceedings - International Conference on Data Engineering20560-571PIDE

Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices

CONSTRUCTION OF EFFICIENT AUTHENTICATION SCHEMES USING TRAPDOOR HASH FUNCTIONS

In large-scale distributed systems, where adversarial attacks can have widespread impact, authentication provides protection from threats involving impersonation of entities and tampering of data. Practical solutions to authentication problems in distributed systems must meet specific constraints of the target system, and provide a reasonable balance between security and cost. The goal of this dissertation is to address the problem of building practical and efficient authentication mechanisms to secure distributed applications. This dissertation presents techniques to construct efficient digital signature schemes using trapdoor hash functions for various distributed applications. Trapdoor hash functions are collision-resistant hash functions associated with a secret trapdoor key that allows the key-holder to find collisions between hashes of different messages. The main contributions of this dissertation are as follows: 1. A common problem with conventional trapdoor hash functions is that revealing a collision producing message pair allows an entity to compute additional collisions without knowledge of the trapdoor key. To overcome this problem, we design an efficient trapdoor hash function that prevents all entities except the trapdoor key-holder from computing collisions regardless of whether collision producing message pairs are revealed by the key-holder. 2. We design a technique to construct efficient proxy signatures using trapdoor hash functions to authenticate and authorize agents acting on behalf of users in agent-based computing systems. Our technique provides agent authentication, assurance of agreement between delegator and agent, security without relying on secure communication channels and control over an agent’s capabilities. 3. We develop a trapdoor hash-based signature amortization technique for authenticating real-time, delay-sensitive streams. Our technique provides independent verifiability of blocks comprising a stream, minimizes sender-side and receiver-side delays, minimizes communication overhead, and avoids transmission of redundant information. 4. We demonstrate the practical efficacy of our trapdoor hash-based techniques for signature amortization and proxy signature construction by presenting discrete log-based instantiations of the generic techniques that are efficient to compute, and produce short signatures. Our detailed performance analyses demonstrate that the proposed schemes outperform existing schemes in computation cost and signature size. We also present proofs for security of the proposed discrete-log based instantiations against forgery attacks under the discrete-log assumption