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25 February 2019
Prasanna Ravi, Sourav Sen Gupta, Anupam Chattopadhyay, Mustafa Khairallah, Zakaria Najm, Shivam Bhasin
Jesper Buus Nielsen, Mark Simkin
In this work, we study leakage-resilient secret sharing schemes with information-theoretic security and prove a lower bound on the share size and the required amount randomness of any such scheme. We prove that for any leakage-resilient secret sharing scheme either the amount of randomness across all shares or the share size has to be linear in $n$. More concretely, for a secret sharing scheme with $p$-bit long shares, $\ell$-bit leakage per share, where $\widehat{t}$ shares uniquely define the remaining $n - \widehat{t}$ shares, it has to hold that $$ p \ge \frac{\ell (n - t)}{\widehat{t}}\ . $$
We use our lower bound to gain further insights into a question that was recently posed by Benhamouda et al. (CRYPTO'18), who ask to what extend existing regular secret sharing schemes already provide protection against leakage. The authors proved that Shamir's secret sharing is leakage-resilient for very large thresholds $t = n - \mathcal{O}(\log n)$ and conjectured that it is also $1$-bit leakage-resilient for any threshold that is a constant fraction of the total number of shares. We do not disprove their conjecture, but show that two mild and natural strengthenings thereof are false, thus concluding that their conjecture is essentially the best one could hope for.
The results described above only apply to secret sharing schemes with information-theoretic security, since the lower bound proof relies on an adversary that can enumerate all possible secret sharings and thus runs in time exponential in at least the share size $p$ and the full reconstruction threshold $\widehat{t}$. In addition, we present a conceptually simple and highly efficient attack for the specific case of $2$-out-of-$n$ Shamir secret sharing that only requires $1$ bit of leakage, has a running time of $\bigO{n}$ field operations, and could easily be run in practice by a computationally bounded adversary.
David Wong
Yue Guo, Rafael Pass, Elaine Shi
Rohit Sinha, Sivanarayana Gaddam, Ranjit Kumaresan
Recently, trusted execution environments (TEEs), such as Intel SGX and Sanctum enclaves, are finding applications in outsourced computing on sensitive data. However, since TEEs are at the mercy of an untrusted host for storage and network communication, they are incapable of enforcing history-dependent policies or fairness. For instance, against a user's wish that only an aggregate function over her entire data is revealed, an adversarial host can repeatedly evaluate that aggregate function on different subsets of her dataset, and learn the individual records. The adversary may also collude and deliver the output only to a subset of the output recipients, thus violating fairness.
This paper presents LucidiTEE, the first system to enable multiple parties to jointly compute on large-scale private data, while guaranteeing that the aforementioned policies are enforced even when the input providers are offline, and guaranteeing fairness to all output recipients. To that end, LucidiTEE develops a set of novel protocols between a network of TEEs and a distributed, shared, append-only ledger. LucidiTEE uses the ledger only to enforce policies; it does not store inputs, outputs, or state on the ledger, nor does it duplicate execution amongst the participants, which allows it to scale to large data and large number of parties.
We demonstrate several policy-based applications including personal finance, federated machine learning, fair $n$-party information exchange, and private set intersection for medical records.
E.V. Flynn, Yan Bo Ti
Nicholas Genise, Craig Gentry, Shai Halevi, Baiyu Li, Daniele Micciancio
Satrajit Ghosh, Mark Simkin
Prior to this work, all previous protocols had a communication complexity of $\Omega(n)$. Our protocols are the first ones with communication complexities that mainly depend on the threshold parameter $t$ and only logarithmically on the set size $n$.
Kasper Green Larsen, Mark Simkin
Vanesa Daza, Alonso González, Zaira Pindado, Carla Ràfols, Javier Silva
Danping Shi, Siwei Sun, Yu Sasaki, Chaoyun Li, Lei Hu
We apply this method to analyze the linear trails of MORUS (one of the seven finalists of the CAESAR competition), which are found with the help of a generic model for linear trails of MORUS-like key-stream generators. In our model, any tool for finding linear trails of block ciphers can be used to search for trails of MORUS-like key-stream generators. As a result, a set of trails with correlation $2^{-38}$ is identified for all versions of full MORUS, while the correlations of previously published best trails for MORUS-640 and MORUS-1280 are $2^{-73}$ and $2^{-76}$ respectively (ASIACRYPT 2018). This significantly improves the complexity of the attack on MORUS-1280-256 from $2^{152}$ to $2^{76}$. These new trails also lead to the first distinguishing and message-recovery attacks on MORUS-640-128 and MORUS-1280-128 with surprisingly low complexities around $2^{76}$.
Moreover, we observe that the condition for exploiting these trails in an attack can be more relaxed than previously thought, which shows that the new trails are superior to previously published ones in terms of both correlation and the number of ciphertext blocks involved.
23 February 2019
Hyderabad, INDIA, 16 December - 20 December 2019
Submission deadline: 12 July 2019
Notification: 25 September 2019
Fuhou, China, 25 October - 27 October 2019
Submission deadline: 6 May 2019
Notification: 8 July 2019
21 February 2019
Department of Engineering at Aarhus University, Denmark
Title: Verifiable cryptographic software
Zero-knowledge proofs are integral for deploying privacy-preserving cryptocurrencies and other blockchain applications, as they represent a fundamental building block for proving statements about confidential data. The most popular framework for such proofs is based on cryptographic pairings defined over elliptic curves, where pairing-based zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) underlie private transactions.
The PhD candidate will investigate techniques to develop a formally verified efficient software library for pairing-based cryptography, as means to support current blockchain projects relying on zero-knowledge proofs. The PhD candidate will also be involved in other educational activities, such as serving as teaching assistant in courses related to his/her expertise.
The project is a collaboration between researchers from the Departments of Engineering and Computer Science; the DIGIT Centre for Digitalisation, Big Data and Data Analytics; and the recently opened Concordium Blockchain Research Center.
Qualifications:
We are looking for dedicated and enthusiastic applicants, preferably with a Master’s degree in Computer Science/Engineering, Mathematics or related discipline. A theoretical background with cryptography or formal verification will be important for the project. Practical experience with software development and the Coq proof assistant will be seen as a plus. Analytical and critical thinking are naturally essential to pursuing a PhD degree. Further requirements are fluency in English, good reporting/organization skills and being able to work independently.
Closing date for applications: 1 May 2019
Contact: Diego F. Aranha, Assistant Professor, dfaranha (at) eng.au.dk; or Bas Spitters, Associate Professor, spitters (at) cs.au.dk
More information: https://phd.scitech.au.dk/for-applicants/apply-here/may-2019/verifiable-cryptographic-software/
University of Warwick
For informal inquiries, please contact Professor Feng Hao, feng.hao (at) warwick.ac.uk, enclosing a CV and a short description of your relevant background and interests.
The Computer Science Department at Warwick is a leading department in the UK. In the 2014 Research Evaluation Framework (REF) which all UK universities participated in, Warwick computer science was ranked the 1st in terms of research output, 2nd in terms of impact and 2nd overall. It is also highly regarded for its research culture, informal environment, excellent students, and beautiful campus.
Closing date for applications: 1 August 2019
More information: https://warwick.ac.uk/fac/sci/dcs/admissions/postgraduateresearch/researchstudentships/?newsItem=8a1785d769003af00169015
Norwegian University of Science and Technology (NTNU)
Closing date for applications: 1 May 2019
Contact: Staal A. Vinterbo, Staal.Vinterbo (at) ntnu.no
More information: https://www.jobbnorge.no/en/available-jobs/job/163521/
Ulm University, Germany
For more details and application portal, see URL below
Closing date for applications: 14 March 2019
Contact: Prof. Dr. Frank Kargl, https://www.uni-ulm.de/in/vs/inst/team/frank-kargl/
More information: https://stellenangebote.uni-ulm.de/jobposting/95503659d66923316e3b202e35ce7405db5365d1
HP Labs, Bristol, UK
Our industrial research lab is a unique environment at the intersection between academic research and real-world innovation in partnership with HP global business units. We provide interns with a unique opportunity to learn about the realities of both worlds, and to contribute research that may eventually impact the HP products and solutions used by millions of people across the globe.
Internships will start between February and July 2019, for a preferred duration of 5-6 months.
We welcome applications from full time students with the relevant skills and experience at Masters and PhD level.
Closing date for applications: 31 March 2019
Contact: Philippa Bayley
Security Lab Operations Manager
philippa.bayley (at) hp.com
More information: https://h30631.www3.hp.com/job/bristol/hp-security-lab-intern/3544/10307305
M. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin Lauter, Farinaz Koushanfar
We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. We further provide a compiler to translate the model description from high-level Python (i.e., Keras) to that of XONN. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7×, MiniONN (ACM CCS'17) by 93×, and SecureML (IEEE S&P'17) by 37×. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis. The datasets include breast cancer, diabetes, liver disease, and Malaria.
20 February 2019
You can register online at https://secure.iacr.org/conferences/fse2019/register/.
FSE 2019 will take place in Paris, France during March 25-28, 2019. For more information on the conference please visit https://fse.iacr.org/2019.