The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Reza Shokri - One of the best experts on this subject based on the ideXlab platform.
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Privacy Games: Optimal User-Centric Data Obfuscation
Proceedings on Privacy Enhancing Technologies, 2015Co-Authors: Reza ShokriAbstract:Consider users who share their data (e.g., lo- cation) with an untrusted service provider to obtain a personalized (e.g., location-based) service. Data obfus- cation is a prevalent user-centric approach to protecting users' privacy in such systems: the untrusted entity only receives a noisy version of user's data. Perturbing data before sharing it, however, comes at the price of the users' utility (service quality) experience which is an in- separable design factor of obfuscation mechanisms. The entanglement of the utility loss and the privacy guaran- tee, in addition to the lack of a comprehensive notion of privacy, have led to the design of obfuscation mecha- nisms that are either suboptimal in terms of their utility loss, or ignore the user's information leakage in the past, or are limited to very specific notions of privacy which e.g., do not protect against adaptive inference attacks or the Adversary with arbitrary background knowledge. In this paper, we design user-centric obfuscation mech- anisms that impose the minimum utility loss for guar- anteeing user's privacy. We optimize utility subject to a joint guarantee of dierential privacy (indistinguishabil- ity) and distortion privacy (inference error). This dou- ble shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint dierential-disto rtion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the dif- ferential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game be- tween the designer of obfuscation mechanism and the Potential Adversary, and design adaptive mechanisms that anticipate and protect against optimal inference al- gorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm.
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privacy games optimal user centric data obfuscation
arXiv: Cryptography and Security, 2014Co-Authors: Reza ShokriAbstract:In this paper, we design user-centric obfuscation mechanisms that impose the minimum utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error). This double shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint differential-distortion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the differential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game between the designer of obfuscation mechanism and the Potential Adversary, and design adaptive mechanisms that anticipate and protect against optimal inference algorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm.
Juels Ari - One of the best experts on this subject based on the ideXlab platform.
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GenoGuard: Protecting Genomic Data against Brute-Force Attacks
2015Co-Authors: Huang Zhicong, Ayday Erman, Fellay Jacques, Hubaux Jean-pierre, Juels AriAbstract:Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the Potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called {\em GenoGuard}, for providing strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In GenoGuard, a Potential Adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard
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GenoGuard: Protecting Genomic Data Against Brute-Force Attacks
2015Co-Authors: Huang Zhicong, Ayday Erman, Fellay Jacques, Hubaux Jean-pierre, Juels AriAbstract:Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the Potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term; given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool called {\em GenoGuard} to provide strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE) that can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can unfortunately be applied to messages from only a very restricted set of probability distributions. GenoGuard thus addresses the open problem of applying HE techniques to the highly non-uniform probability distributions characterizing sequences of genetic data. In GenoGuard, a Potential Adversary can attempt to guess keys or passwords exhaustively and decrypt via a brute-force attack. We prove that decryption under any key, however, will yield a plausible genome sequence; thus GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks using side information. Finally, we present an efficient and parallelized software implementation of GenoGuard
Haoxuan Zheng - One of the best experts on this subject based on the ideXlab platform.
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ultrafast quantum random number generation based on quantum phase fluctuations
Optics Express, 2012Co-Authors: Haoxuan ZhengAbstract:A quantum random number generator (QRNG) can generate true randomness by exploiting the fundamental indeterminism of quantum mechanics. Most approaches to QRNG employ single-photon detection technologies and are limited in speed. Here, we experimentally demonstrate an ultrafast QRNG at a rate over 6 Gbits/s based on the quantum phase fluctuations of a laser operating near threshold. Moreover, we consider a Potential Adversary who has partial knowledge on the raw data and discuss how one can rigorously remove such partial knowledge with postprocessing. We quantify the quantum randomness through min-entropy by modeling our system and employ two randomness extractors--Trevisan's extractor and Toeplitz-hashing--to distill the randomness, which is information-theoretically provable. The simplicity and high-speed of our experimental setup show the feasibility of a robust, low-cost, high-speed QRNG.
Huang Zhicong - One of the best experts on this subject based on the ideXlab platform.
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GenoGuard: Protecting Genomic Data against Brute-Force Attacks
2015Co-Authors: Huang Zhicong, Ayday Erman, Fellay Jacques, Hubaux Jean-pierre, Juels AriAbstract:Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the Potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called {\em GenoGuard}, for providing strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In GenoGuard, a Potential Adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard
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GenoGuard: Protecting Genomic Data Against Brute-Force Attacks
2015Co-Authors: Huang Zhicong, Ayday Erman, Fellay Jacques, Hubaux Jean-pierre, Juels AriAbstract:Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the Potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term; given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool called {\em GenoGuard} to provide strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE) that can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can unfortunately be applied to messages from only a very restricted set of probability distributions. GenoGuard thus addresses the open problem of applying HE techniques to the highly non-uniform probability distributions characterizing sequences of genetic data. In GenoGuard, a Potential Adversary can attempt to guess keys or passwords exhaustively and decrypt via a brute-force attack. We prove that decryption under any key, however, will yield a plausible genome sequence; thus GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks using side information. Finally, we present an efficient and parallelized software implementation of GenoGuard
Milind Tambe - One of the best experts on this subject based on the ideXlab platform.
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The human element: addressing human adversaries in security domains
2012Co-Authors: Milind Tambe, James PitaAbstract:Recently, game theory has been shown to be useful for reasoning about real-world security settings where security forces must protect critical assets from Potential adversaries. In fact, there have been a number of deployed real-world applications of game theory for security (e.g., ARMOR at Los Angeles International Airport and IRIS for the Federal Air Marshals Service). Here, the objective is for the security force to utilize its limited resources to best defend their critical assets. An important factor in these real-world security settings is that the adversaries involved are humans who may not behave according to the standard assumptions of game-theoretic models. There are two key shortcomings of the approaches currently employed in these recent applications. First, human adversaries may not make the predicted rational decision. In such situations, where the security force has optimized against a perfectly rational opponent, a deviation by the human Adversary can lead to adverse affects on the security force's predicted outcome. Second, human adversaries are naturally creative and security domains are highly dynamic, making enumeration of all Potential threats a practically impossible task and solving the resulting game, with current leading approaches, would be intractable. My thesis contributes to a very new area that combines algorithmic and experimental game theory. Indeed, it examines a critical problem in applying game-theoretic techniques to situations where perfectly rational solvers must address human adversaries. In doing so it advances the study and reach of game theory to domains where software agents and humans may interact. More specifically, to address the first shortcoming, my thesis presents two separate algorithms to address Potential deviations from the predicted rational decision by human adversaries. Experimental results, from a simulation that is motivated by a real-world security domain at Los Angeles International airport, demonstrated that both of my approaches outperform the currently deployed optimal algorithms which utilize standard game-theoretic assumptions and additional alternative algorithms against humans. In fact, one of my approaches is currently under evaluation in a real-world application to aid in resource allocation decisions for the United States Coast Guard. Towards addressing the second shortcoming of enumeration of a large number of Potential Adversary threat capabilities, I introduce a new game-theoretic model for efficiency, which additionally generalizes the previously accepted model for security domains. This new game-theoretic model for addressing human threat capabilities has seen real-world deployment and is under evaluation to aid the United States Transportation Security Administration in their resource allocation challenges.
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Security and game theory: Algorithms, deployed systems, lessons learned
Security and Game Theory: Algorithms Deployed Systems Lessons Learned, 2011Co-Authors: Milind TambeAbstract:Global threats of terrorism, drug-smuggling, and other crimes have led to a significant increase in research on game theory for security. Game theory provides a sound mathematical approach to deploy limited security resources to maximize their effectiveness. A typical approach is to randomize security schedules to avoid predictability, with the randomization using artificial intelligence techniques to take into account the importance of different targets and Potential Adversary reactions. This book distills the forefront of this research to provide the first and only study of long-term deployed applications of game theory for security for key organizations such as the Los Angeles International Airport police and the U.S. Federal Air Marshals Service. The author and his research group draw from their extensive experience working with security officials to intelligently allocate limited security resources to protect targets, outlining the applications of these algorithms in research and the real world. The book also includes professional perspectives from security experts Erroll G. Southers; Lieutenant Commander Joe DiRenzo III, U.S. Coast Guard; Lieutenant Commander Ben Maule, U.S. Coast Guard; Erik Jensen, U.S. Coast Guard; and Lieutenant Fred S. Bertsch IV, U.S. Coast Guard.
