Characterizing the Impact of Network Delay on Bitcoin Mining

Scientific Conference (2021, September)

DyPS: Dynamic, Private and Secure GWAS

in Proceedings on Privacy Enhancing Technologies (2021)

Genome-Wide Association Studies (GWAS) identify the genomic variations that are statistically associated with a particular phenotype (e.g., a disease). The confidence in GWAS results increases with the ... [more ▼]

Genome-Wide Association Studies (GWAS) identify the genomic variations that are statistically associated with a particular phenotype (e.g., a disease). The confidence in GWAS results increases with the number of genomes analyzed, which encourages federated computations where biocenters would periodically share the genomes they have sequenced. However, for economical and legal reasons, this collaboration will only happen if biocenters cannot learn each others’ data. In addition, GWAS releases should not jeopardize the privacy of the individuals whose genomes are used. We introduce DyPS, a novel framework to conduct dynamic privacy-preserving federated GWAS. DyPS leverages a Trusted Execution Environment to secure dynamic GWAS computations. Moreover, DyPS uses a scaling mechanism to speed up the releases of GWAS results according to the evolving number of genomes used in the study, even if individuals retract their participation consent. Lastly, DyPS also tolerates up to all-but-one colluding biocenters without privacy leaks. We implemented and extensively evaluated DyPS through several scenarios involving more than 6 million simulated genomes and up to 35,000 real genomes. Our evaluation shows that DyPS updates test statistics with a reasonable additional request processing delay (11% longer) compared to an approach that would update them with minimal delay but would lead to 8% of the genomes not being protected. In addition, DyPS can result in the same amount of aggregate statistics as a static release (i.e., at the end of the study), but can produce up to 2.6 times more statistics information during earlier dynamic releases. Besides, we show that DyPS can support a larger number of genomes and SNP positions without any significant performance penalty. [less ▲]

PriLok:Citizen-protecting distributed epidemic tracing

E-print/Working paper (2020)

Contact tracing is an important instrument for national health services to fight epidemics. As part of the COVID-19 situation, many proposals have been made for scaling up contract tracing capacities with ... [more ▼]

Contact tracing is an important instrument for national health services to fight epidemics. As part of the COVID-19 situation, many proposals have been made for scaling up contract tracing capacities with the help of smartphone applications, an important but highly critical endeavor due to the privacy risks involved in such solutions. Extending our previously expressed concern, we clearly articulate in this article, the functional and non-functional requirements that any solution has to meet, when striving to serve, not mere collections of individuals, but the whole of a nation, as required in face of such potentially dangerous epidemics. We present a critical information infrastructure, PriLock, a fully-open preliminary architecture proposal and design draft for privacy preserving contact tracing, which we believe can be constructed in a way to fulfill the former requirements. Our architecture leverages the existing regulated mobile communication infrastructure and builds upon the concept of "checks and balances", requiring a majority of independent players to agree to effect any operation on it, thus preventing abuse of the highly sensitive information that must be collected and processed for efficient contact tracing. This is enforced with a largely decentralised layout and highly resilient state-of-the-art technology, which we explain in the paper, finishing by giving a security, dependability and resilience analysis, showing how it meets the defined requirements, even while the infrastructure is under attack. [less ▲]

Privacy-Preserving Processing of Filtered DNA Reads

Scientific Conference (2019, October 22)

DNA-SeAl: Sensitivity Levels to Optimize the Performance of Privacy-Preserving DNA Alignment

in IEEE Journal of Biomedical and Health Informatics (2019)

The advent of next-generation sequencing (NGS) machines made DNA sequencing cheaper, but also put pressure on the genomic life-cycle, which includes aligning millions of short DNA sequences, called reads ... [more ▼]

The advent of next-generation sequencing (NGS) machines made DNA sequencing cheaper, but also put pressure on the genomic life-cycle, which includes aligning millions of short DNA sequences, called reads, to a reference genome. On the performance side, efficient algorithms have been developed, and parallelized on public clouds. On the privacy side, since genomic data are utterly sensitive, several cryptographic mechanisms have been proposed to align reads more securely than the former, but with a lower performance. This manuscript presents DNA-SeAl a novel contribution to improving the privacy × performance product in current genomic workflows. First, building on recent works that argue that genomic data needs to be treated according to a threat-risk analysis, we introduce a multi-level sensitivity classification of genomic variations designed to prevent the amplification of possible privacy attacks. We show that the usage of sensitivity levels reduces future re-identification risks, and that their partitioning helps prevent linkage attacks. Second, after extending this classification to reads, we show how to align and store reads using different security levels. To do so, DNA-SeAl extends a recent reads filter to classify unaligned reads into sensitivity levels, and adapts existing alignment algorithms to the reads sensitivity. We show that using DNA-SeAl allows high performance gains whilst enforcing high privacy levels in hybrid cloud environments. [less ▲]

RepuCoin: Your Reputation is Your Power

in IEEE Transactions on Computers (2019)

Existing proof-of-work cryptocurrencies cannot tolerate attackers controlling more than 50% of the network’s computing power at any time, but assume that such a condition happening is “unlikely”. However ... [more ▼]

Existing proof-of-work cryptocurrencies cannot tolerate attackers controlling more than 50% of the network’s computing power at any time, but assume that such a condition happening is “unlikely”. However, recent attack sophistication, e.g., where attackers can rent mining capacity to obtain a majority of computing power temporarily, render this assumption unrealistic. This paper proposes RepuCoin, the first system to provide guarantees even when more than 50% of the system’s computing power is temporarily dominated by an attacker. RepuCoin physically limits the rate of voting power growth of the entire system. In particular, RepuCoin defines a miner’s power by its ‘reputation’, as a function of its work integrated over the time of the entire blockchain, rather than through instantaneous computing power, which can be obtained relatively quickly and/or temporarily. As an example, after a single year of operation, RepuCoin can tolerate attacks compromising 51% of the network’s computing resources, even if such power stays maliciously seized for almost a whole year. Moreover, RepuCoin provides better resilience to known attacks, compared to existing proof-of-work systems, while achieving a high throughput of 10000 transactions per second (TPS). [less ▲]

Accurate filtering of privacy-sensitive information in raw genomic data

in Journal of Biomedical Informatics (2018)

Sequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data ... [more ▼]

Sequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption. [less ▲]

Enclave-Based Privacy-Preserving Alignment of Raw Genomic Information

Scientific Conference (2017, October)

Recent breakthroughs in genomic sequencing led to an enormous increase of DNA sampling rates, which in turn favored the use of clouds to efficiently process huge amounts of genomic data. However, while ... [more ▼]

Recent breakthroughs in genomic sequencing led to an enormous increase of DNA sampling rates, which in turn favored the use of clouds to efficiently process huge amounts of genomic data. However, while allowing possible achievements in personalized medicine and related areas, cloud-based processing of genomic information also entails significant privacy risks, asking for increased protection. In this paper, we focus on the first, but also most data-intensive, processing step of the genomics information processing pipeline: the alignment of raw genomic data samples (called reads) to a synthetic human reference genome. Even though privacy-preserving alignment solutions (e.g., based on homomorphic encryption) have been proposed, their slow performance encourages alternatives based on trusted execution environments, such as Intel SGX, to speed up secure alignment. Such alternatives have to deal with data structures whose size by far exceeds secure enclave memory, requiring the alignment code to reach out into untrusted memory. We highlight how sensitive genomic information can be leaked when those enclave-external alignment data structures are accessed, and suggest countermeasures to prevent privacy breaches. The overhead of these countermeasures indicate that the competitiveness of a privacy-preserving enclave-based alignment has yet to be precisely evaluated. [less ▲]

Meeting the Challenges of Critical and Extreme Dependability and Security

in Proceedings of the 22nd Pacific Rim International Symposium on Dependable Computing (2017)

The world is becoming an immense critical information infrastructure, with the fast and increasing entanglement of utilities, telecommunications, Internet, cloud, and the emerging IoT tissue. This may ... [more ▼]

The world is becoming an immense critical information infrastructure, with the fast and increasing entanglement of utilities, telecommunications, Internet, cloud, and the emerging IoT tissue. This may create enormous opportunities, but also brings about similarly extreme security and dependability risks. We predict an increase in very sophisticated targeted attacks, or advanced persistent threats (APT), and claim that this calls for expanding the frontier of security and dependability methods and techniques used in our current CII. Extreme threats require extreme defenses: we propose resilience as a unifying paradigm to endow systems with the capability of dynamically and automatically handling extreme adversary power, and sustaining perpetual and unattended operation. In this position paper, we present this vision and describe our methodology, as well as the assurance arguments we make for the ultra-resilient components and protocols they enable, illustrated with case studies in progress. [less ▲]

Avoiding Leakage and Synchronization Attacks through Enclave-Side Preemption Control

Scientific Conference (2016, December 12)

Intel SGX is the latest processor architecture promising secure code execution despite large, complex and hence potentially vulnerable legacy operating systems (OSs). However, two recent works identified ... [more ▼]

Intel SGX is the latest processor architecture promising secure code execution despite large, complex and hence potentially vulnerable legacy operating systems (OSs). However, two recent works identified vulnerabilities that allow an untrusted management OS to extract secret information from Intel SGX's enclaves, and to violate their integrity by exploiting concurrency bugs. In this work, we re-investigate delayed preemption (DP) in the context of Intel SGX. DP is a mechanism originally proposed for L4-family microkernels as disable-interrupt replacement. Recapitulating earlier results on language-based information-flow security, we illustrate the construction of leakage-free code for enclaves. However, as long as adversaries have fine-grained control over preemption timing, these solutions are impractical from a performance/complexity perspective. To overcome this, we resort to delayed preemption, and sketch a software implementation for hypervisors providing enclaves as well as a hardware extension for systems like SGX. Finally, we illustrate how static analyses for SGX may be extended to check confidentiality of preemption-delaying programs. [less ▲]