Cryptology ePrint Archive: Report 2015/332

Security Intelligence for Broadcast : Threat Analytics

Sumit Chakraborty

Abstract: Abstract: This work presents an Adaptively Secure Broadcast Mechanism (ASBM) based on threats analytics. It defines the security intelligence of a broadcast system comprehensively with a novel concept of collective intelligence. The algorithmic mechanism is analyzed from the perspectives of security intelligence, communication complexity and computational intelligence. The security intelligence of ASBM is defined in terms of authentication, authorization, correct identification, privacy: group, forward and backward, confidentiality and audit; fairness, correctness, transparency, accountability, trust, non-repudiation and data integrity; reliability, consistency, liveness, deadlock-freeness, safety and reachability. The computational intelligence is associated with the complexity of broadcast scheduling, verification of security intelligence of broadcasting system, key management strategies and payment function computation. The cost of communication depends on number of agents and subgroups in the broadcasting group and complexity of data. The business intelligence depends on payment function and quality of data stream. ASBM recommends a set of intelligent model checking moves for the verification of security intelligence of the broadcasting system. The primary objective of ASBM is to improve the quality of broadcast through fundamental rethinking and radical redesign of a reliable communication schema. This work also outlines the architecture of an automated system verification tool for the protection of the broadcasting system. In the existing works of adaptively secure broadcast, broadcast corruption is not assessed properly. The issues of broadcast corruption have been defined imprecisely and incompletely through statistical reasoning. A broadcast protocol allows a sender to distribute a secret through a point-to-point network to a set of recipients such that (i) all recipients get the same data even if the sender is corrupted and (ii) it is the senderís data if it is honest. Broadcast protocols satisfying these properties are known to exist if and only if t < n/3, where n denotes the total number of parties, and t denotes the maximal number of corruptions. When a setup allowing signatures is available to the parties, then such protocols exist even for t < n. In the current work, the flaws of aforesaid bounds are corrected through case based reasoning of miscellaneous broadcast applications technically through a set of test cases. It is not rational to state the bound of adaptively secure broadcast protocol in a simple straight forward way. Adaptively secure broadcast mechanism (ASBM) results correct and fair output if and only if all the agents (sending agent, receiving agents and broadcast system administrator), communication channel, broadcast mechanism, broadcast data, payment function and payment mechanism are free of corruption. Here, the risks of broadcast corruption are assessed and mitigated through collective security intelligence on ASBM. First, this works designs ASBM which is more complex than the existing adaptively secure broadcast protocol and then explores the corruption of ASBM from different angles. The concept of collective security intelligence is important to design robust, stable and secure auction, reverse auction, combinatorial auction and multi-party negotiation protocols in various types of broadcast applications. An isolated approach or focus on a specific type of threats cannot solve the ultimate problem of adaptively secure broadcast. ASBM is applicable to the analysis of intelligent mechanisms in static and dynamic networks, auction or combinatorial auction for e-market, digital content distribution through computational advertising, cloud computing, radio and digital TV broadcast, SCADA and sensor networks.

Category / Keywords: Broadcast Mechanism, Security intelligence, Computational intelligence, Communication complexity, Threat analytics, Automated system verification

Date: received 13 Apr 2015, last revised 1 Jun 2016

Contact author: schakraborty2010 at hotmail com

Available format(s): PDF | BibTeX Citation

Note: The work is revised and improved by including various relevant examples and design of efficient algorithmic mechanism.

Version: 20160601:123631 (All versions of this report)

Short URL: ia.cr/2015/332

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