Confidentialité | EPFL Graph Search

La confidentialité est définie par l'Organisation internationale de normalisation (ISO) comme , et est une des pierres angulaires de la sécurité de l'information. La confidentialité est l'une des raisons d'être des cryptosystèmes, rendus possibles dans la pratique par les techniques de la cryptographie moderne. La confidentialité est également un principe éthique associé à plusieurs professions, notamment dans les domaines de la médecine, du droit, de la vente, de l'informatique, de la religion, du journalisme, du . En éthique et en droit, certains types de communication entre une personne et un de ces professionnels sont dites « privilégiées » et ne peuvent être discutées avec, ou divulguées à des tierces parties. Dans certaines juridictions où la loi assure une telle confidentialité, des sanctions sont habituellement prévues en cas d'infraction. Confidentialité journalistique Les journalistes distinguent souvent plusieurs degrés de confidentialité avec les sources d'in

The triangular value of patents

Albina Khairullina

This thesis addresses the question of a patent value from three different angles. It comprises three papers on the patent valuation methods. The patent valuation issues are well-known to the world of research and practice. However, the debates over what the patent contributes to the business and the economy overall remain open. The main contribution of my dissertation is that I present novel ways of addressing the value of patents from both micro- and macroeconomic perspectives. I bridge the theoretical modeling with real-world estimations, which sheds light on the legitimacy of certain hypothetical assumptions in patent valuation. In the first paper, I develop a factor model to value patents in a corporate patent portfolio. According to the results, the patent value is determined by five factors, such as forward and backward citations, family size, the number of oppositions received, and the number of claims. I contribute to the literature by introducing an interaction of a quantitative measure of a patent value with its qualitative assessment derived from a survey of patent managers. The results show that model estimates are weakly consistent with an expert opinion on the value of patents. In the second paper, we collaborate with a large multinational company and analyze internal and highly confidential data on product sales, revenues, and other corporate performance measures by tracing each patent down to its product market. To establish causality, we design a randomized controlled trial together with the management of the company, in which some randomly chosen patents are dropped from the portfolio, while others are kept for the duration of the study. To our knowledge, this is the first study that conducts a randomized controlled trial to measure the real market effect of patent protection. In the third paper, we study whether the regulator expands production possibilities of the economy by assigning a standard-essential status to patents. As we show, the innovatorsâ risk of losing the standard-setting game ex-ante attenuates the anticipation of a larger market share. Secondly, since the discovery of new technologies is typically slower than the discounting rate for the monopoly profits, standards with a positive contribution to productivity tend to be growth-reducing. Our results have significant policy implications. Regulators impose Fair, Reasonable, And Non-Discriminatory (FRAND) pricing on standard-essential technologies to compensate for the larger market-share the innovators get. We demonstrate how FRAND regulation of mark-ups has an unambiguously negative effect on growth and long-term growth-destroying consequences.

EPFL2020

Subdomain and Access Pattern Privacy Trading off Confidentiality and Performance

Matus Harvan, Thomas Locher, Bin Lu

Homomorphic encryption and secure multi-party computation enable computations on encrypted data. However, both techniques suffer from a large performance overhead. While advances in algorithms might reduce the overhead, we show that achieving perfect (or even computational) confidentiality is not possible without increasing the running time compared to computations on plaintext more than exponentially in some cases. In practice, however, perfect confidentiality is not always required. The paper discusses mechanisms to trade off confidentiality and performance for computing on ciphertexts. It introduces a fine-grained approach to define security levels for variables called (statistical) subdomain privacy. This concept differs substantially from prior work because it treats a variable as confidential or non-confidential depending on the actual value. We further propose privacy-preserving methods for memory access patterns. We apply our techniques to improve performance of control flow logic (loops, if-then-else logic) and arithmetic operations such as multiplications. The evaluation shows that the resulting speedup can be in the order of several magnitudes depending on the privacy needs.

Scitepress2016

Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption

David Jules Froelicher

Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the sharing and outsourcing of personal information among stakeholders that are in different jurisdictions. Sharing data is, however, required in many domains. The medical sector is a paradigmatic example: Privacy is paramount and data sharing is needed in numerous applications where data is scarce and scattered among multiple stakeholders around the world. Existing privacy-preserving solutions for federated analytics (FA) rely either (1) on data centralization or outsourcing to a limited number of entities, which incur multiple security and trust issues, or (2) on the exchange of cleartext aggregated and optionally obfuscated data, which can leak personal information or introduce bias in the final result. In this thesis, our goal is (1) to propose privacy-preserving federated solutions for exploration, and for statistical and machine-learning analyses on data held by multiple distrustful stakeholders, and (2) to analyze and evaluate the proposed systems, thus showing that they provide an efficient, secure, scalable, and accurate alternative to existing solutions for FA by proving their utility in real-world state-of-the-art biomedical studies. We rely on multiparty homomorphic encryption (MHE). MHE combines secure multiparty computation (SMC) techniques with homomorphic encryption (HE) by pooling the advantages of both SMC and HE, i.e., interactivity and flexibility, and by minimizing their disadvantages, i.e., difficulty in scaling to a large number of parties and computation complexity.First, we design UnLynx, a system that enables privacy-preserving federated data exploration on a distributed dataset held by multiple data-providers (DPs), where N-1 out of N of the nodes performing the computations can be malicious. We build interactive protocols by relying on ElGamal additive homomorphic encryption (AHE) and ensure that each untrusted-node operation can be publicly verified by means of zero-knowledge proofs (ZKPs). We then explore how statistics, e.g., standard deviation and variance, can be computed by relying on AHE and ZKPs through the design of another system named Drynx. In Drynx, we also explore how to limit the influence of an entity that inputs wrong data in the system, and we propose an efficient federated solution for correctness verification.We propose Spindle a solution for secure cooperative gradient descent on federated data that we instantiate for the privacy-preserving training and oblivious evaluation of generalized linear models. Spindle covers the entire machine-learning workflow, as it enables oblivious predictions to be performed on a trained model that remains secret. It ensures both data and model confidentiality in a passive adversarial model in which N-1 out of N DPs can collude.Finally, we demonstrate that the solutions proposed in this thesis can be efficient enablers for large-scale, sensitive, multi-site biomedical studies. We design and test, by replicating recent medical studies, secure workflows for the federated execution of computations that span from analyses with low computational complexity, such as survival analyses, to analyses with high computational complexity such as genome-wide association studies on millions of variants.

EPFL2021