Security of Symmetric Encryption against Mass Surveillance

Bellare, Mihir; Paterson, Kenneth G.; Rogaway, Phillip

doi:10.1007/978-3-662-44371-2_1

Cited by 140 publications

(217 citation statements)

References 42 publications

(55 reference statements)

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“…In fact, under certain assumptions that are commonly satisfied in practice, we show that a stable training algorithm (i.e., one that does not overfit) can be subverted so that the resulting model is nearly as stable but reveals exact membership information through its black-box behavior. This attack is suggestive of algorithm substitution attacks from cryptography [23] and makes adversarial assumptions similar to those of other recent ML privacy attacks [24]. We implement this construction to train deep CNNs (Section 6.4) and observe that, regardless of the model's generalization behavior, the attacker can recover membership information while incurring very little penalty to predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom

Giacomelli

Fredrikson

et al. 2018

2018 IEEE 31st Computer Security Foundations Symposium (CSF)

631

787

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Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models' structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role.This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks. * This is the unabridged version of the paper accepted for publication in CSF 2018.

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Section: Introductionmentioning

confidence: 99%

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom

Giacomelli

Fredrikson

et al. 2018

2018 IEEE 31st Computer Security Foundations Symposium (CSF)

631

787

View full text Add to dashboard Cite

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“…But Lemma 8 states just that. We thus have Adv cca A,PKES * ,C (κ) + negl(κ) ≥ Adv (5) W (κ). As the system PKES * is CCAsecure by Corollary 6, this advantage is negligible.…”

Section: Indistinguishability Of H 2 Andmentioning

confidence: 98%

“…We construct an attacker A on PKES * such that there is a negligible function negl with Adv cca A,PKES * ,C (κ)+negl(κ) ≥ Adv (5) W (κ). Note that such an attacker A has access to the decryption-oracle PKES * .Dec sk (·).…”

Section: Indistinguishability Of H 2 Andmentioning

confidence: 99%

On the Gold Standard for Security of Universal Steganography

Berndt

Liśkiewicz

2018

Advances in Cryptology – EUROCRYPT 2018

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While symmetric-key steganography is quite well understood both in the information-theoretic and in the computational setting, many fundamental questions about its public-key counterpart resist persistent attempts to solve them. The computational model for public-key steganography was proposed by von Ahn and Hopper in EUROCRYPT 2004. At TCC 2005, Backes and Cachin gave the first universal public-key stegosystem -i. e. one that works on all channels -achieving security against replayable chosen-covertext attacks (SS-RCCA) and asked whether security against non-replayable chosen-covertext attacks (SS-CCA) is achievable. Later, Hopper (ICALP 2005) provided such a stegosystem for every efficiently sampleable channel, but did not achieve universality. He posed the question whether universality and SS-CCA-security can be achieved simultaneously. No progress on this question has been achieved since more than a decade. In our work we solve Hopper's problem in a somehow complete manner: As our main positive result we design an SS-CCA-secure stegosystem that works for every memoryless channel. On the other hand, we prove that this result is the best possible in the context of universal steganography. We provide a family of 0-memoryless channels -where the already sent documents have only marginal influence on the current distribution -and prove that no SS-CCA-secure steganography for this family exists in the standard non-look-ahead model.

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“…The non-deterministic behavior of IP cores and/or their functional specification which accepts small probabilistic fluctuations within some acceptable range allows a covert channel for H N D Trojans to embed some minimal malicious payload in the standard output without being detected by an external observer [10]. The external observer considers these small fluctuations as part of the functional specification.…”

Section: H D Vs H N D Hardware Trojansmentioning

confidence: 99%

Advancing the state-of-the-art in hardware Trojans design

Haider

Jin

Dijk

2017

2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Abstract-Electronic Design Automation (EDA) industry heavily reuses third party IP cores. These IP cores are vulnerable to insertion of Hardware Trojans (HTs) at design time by third party IP core providers or by malicious insiders in the design team. State of the art research has shown that existing HT detection techniques, which claim to detect all publicly available HT benchmarks, can still be defeated by carefully designing new sophisticated HTs. The reason being that these techniques consider the HT landscape to be limited only to the publicly known HT benchmarks, or other similar (simple) HTs. However the adversary is not limited to these HTs and may devise new HT design principles to bypass these countermeasures. In this paper, we discover certain crucial properties of HTs which lead to the definition of an exponentially large class of Deterministic Hardware Trojans H D that an adversary can (but is not limited to) design. The discovered properties serve as HT design principles, based on which we design a new HT called XOR-LFSR and present it as a 'proof-of-concept' example from the class H D . These design principles help us understand the tremendous ways an adversary has to design a HT, and show that the existing publicly known HT benchmarks are just the tip of the iceberg on this huge landscape. This work, therefore, stresses that instead of guaranteeing a certain (low) false negative rate for a small constant set of publicly known HTs, a rigorous HT detection tool should take into account these newly discovered HT design principles and hence guarantee the detection of an exponentially large class (exponential in number of wires in IP core) of HTs with negligible false negative rate.

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Security of Symmetric Encryption against Mass Surveillance

Cited by 140 publications

References 42 publications

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

On the Gold Standard for Security of Universal Steganography

Advancing the state-of-the-art in hardware Trojans design

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