Institutionalizing ADR: Clashing Values

2021 ◽  
pp. 391-396
Author(s):  
Carrie Menkel-Meadow
Keyword(s):  
Adversary Model

I appreciate the thoughtful reflections of Jim Coben, Ellen Deason, and Elayne Greenberg on the teachings and legacies of my 1991 article, which attempted to capture the beginning of several types of institutionalization and co-optation of some of the key ideas of the “ADR movement.” Sadly, much of what I commented on then has, in my view, only gotten worse as the adversary model is gaining, not losing, ascendancy (...

scholarly journals End-to-End Game-Focused Learning of Adversary Behavior in Security Games

2020 ◽  
Vol 34 (02) ◽  
pp. 1378-1386
Author(s):  
Andrew Perrault ◽  
Bryan Wilder ◽  
Eric Ewing ◽  
Aditya Mate ◽  
Bistra Dilkina ◽  
...  
Keyword(s):  
Expected Utility ◽  
Optimization Problem ◽  
Predictive Accuracy ◽  
Experimental Results ◽  
Limited Data ◽  
Two Stage ◽  
Security Games ◽  
End To End ◽  
Feature Values ◽  
Adversary Model

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem. We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility. We show both in theory and experimental results that our game-focused approach achieves higher defender expected utility than the two-stage alternative when there is limited data.

Fully Secure Codes Based Tracing and Revoking Scheme with Constant Ciphertext

2014 ◽  
Vol 543-547 ◽  
pp. 3300-3307 ◽  
Author(s):  
Xing Wen Zhao ◽  
Gao Fei Zhao ◽  
Hui Li
Keyword(s):  
Random Oracle ◽  
Broadcast Encryption ◽  
Traitor Tracing ◽  
Encryption Scheme ◽  
User Revocation ◽  
New Construction ◽  
Identity Based ◽  
Traitor Tracing Schemes ◽  
Adaptive Adversary ◽  
Adversary Model

In broadcast encryption system certain users may leak their decryption keys to build pirate decoders, so traitor tracing is quite necessary. There exist many codes based traitor tracing schemes. As pointed out by Billet and Phan in ICITS 2008, these schemes lack revocation ability. The ability of revocation can disable identified malicious users and users who fail to fulfill the payments, so that the broadcast encryption system can be more practical. Recently, Zhao and Li presented a construction of codes based tracing and revoking scheme which achieves user revocation as well as traitor tracing. However, their scheme is only secure against chosen plaintext attacks under selective-adversary model with random oracle. In this paper, we obtain a new construction of codes based tracing and revoking scheme which is proved secure against chosen ciphertext attacks under adaptive-adversary model without random oracle. Our idea is to insert codeword into Boneh and Hamburgs identity based broadcast encryption scheme to retain the ability of user revocation and use Boneh and Naors method to trace traitors. Our fully secure scheme is roughly as efficient as Zhao and Lis scheme while the security is enhanced.

scholarly journals Practicalm-k-Anonymization for Collaborative Data Publishing without Trusted Third Party

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jingyu Hua ◽  
An Tang ◽  
Qingyun Pan ◽  
Kim-Kwang Raymond Choo ◽  
Hong Ding ◽  
...  
Keyword(s):  
Third Party ◽  
Data Publishing ◽  
Trusted Third Party ◽  
Adversary Model

In collaborative data publishing (CDP), anm-adversary attack refers to a scenario where up tommalicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributedk-anonymization scheme,m-k-anonymization, designed to defend againstm-adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset.

An adversary model to evaluate DRM protection of video contents on iOS devices

2016 ◽  
Vol 56 ◽  
pp. 94-110 ◽  
Author(s):  
Christian D'Orazio ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Adversary Model

scholarly journals LifeLine for FPGA Protection: Obfuscated Cryptography for Real-World Security

2021 ◽  
pp. 412-446
Author(s):  
Florian Stolz ◽  
Nils Albartus ◽  
Julian Speith ◽  
Simon Klix ◽  
Clemens Nasenberg ◽  
...  
Keyword(s):  
Real World ◽  
Intrinsic Structure ◽  
Protection Mechanism ◽  
Persistent Problem ◽  
Review State ◽  
Hardware Designs ◽  
Design Protection ◽  
Intellectual Property Infringement ◽  
Adversary Model ◽  
Integration Effort

Over the last decade attacks have repetitively demonstrated that bitstream protection for SRAM-based FPGAs is a persistent problem without a satisfying solution in practice. Hence, real-world hardware designs are prone to intellectual property infringement and malicious manipulation as they are not adequately protected against reverse-engineering.In this work, we first review state-of-the-art solutions from industry and academia and demonstrate their ineffectiveness with respect to reverse-engineering and design manipulation. We then describe the design and implementation of novel hardware obfuscation primitives based on the intrinsic structure of FPGAs. Based on our primitives, we design and implement LifeLine, a hardware design protection mechanism for FPGAs using hardware/software co-obfuscated cryptography. We show that LifeLine offers effective protection for a real-world adversary model, requires minimal integration effort for hardware designers, and retrofits to already deployed (and so far vulnerable) systems.

scholarly journals Adversary Models for Mobile Device Authentication

2022 ◽  
Vol 54 (9) ◽  
pp. 1-35
Author(s):  
René Mayrhofer ◽  
Stephan Sigg
Keyword(s):  
Mobile Device ◽  
User Authentication ◽  
Security Analysis ◽  
Highly Active ◽  
Remote Authentication ◽  
Security Properties ◽  
Active Research ◽  
Adversary Model ◽  
Vast Range

Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.

scholarly journals Re-Identification of Individuals in Genomic Data-Sharing Beacons via Allele Inference

2017 ◽  
Author(s):  
Nora von Thenen ◽  
Erman Ayday ◽  
A. Ercument Cicek
Keyword(s):  
Data Sharing ◽  
Genomic Data ◽  
Nucleotide Polymorphisms ◽  
Privacy Risk ◽  
High Confidence ◽  
Single Nucleotide ◽  
Order Markov Chain ◽  
Adversary Model ◽  
Very High ◽  
Membership Information

AbstractGenomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his/her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95% confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5% of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model.

Energy-Aware Green Adversary Model for Cyberphysical Security in Industrial System

2020 ◽  
Vol 16 (5) ◽  
pp. 3322-3329 ◽  
Author(s):  
Arun Kumar Sangaiah ◽  
Darshan Vishwasrao Medhane ◽  
Gui-Bin Bian ◽  
Ahmed Ghoneim ◽  
Mubarak Alrashoud ◽  
...  
Keyword(s):  
Industrial System ◽  
Energy Aware ◽  
Adversary Model
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