We introduce the notion of sanitizable signatures that offer many attractive security features for certain current and emerging applications. A sanitizable signature allows authorized semi-trusted censors to modify – in a limited and controlled fashion – parts of a signed message without interacting with the original signer. We present constructions for this new primitive, based on standard signature schemes and secure under common cryptographic assumptions. We also provide experimental measurements for the implementation of a sanitizable signature scheme and demonstrate its practicality.

Sanitizable signatures

Encouraging the release of network data is central to promoting sound network research practices, though the publication of this data can leak sensitive information about the publishing organization. To address this dilemma, several techniques have been suggested for anonymizing network data by obfuscating sensitive fields. In this paper, we present new techniques for inferring network topology and deanonymizing servers present in anonymized network data, using only the data itself and public information. Via analyses on three different network datasets, we quantify the effectiveness of our techniques, showing that they can uncover significant amounts of sensitive information. We also discuss prospects for preventing these deanonymization attacks.

/pdf/playing-devil-s-advocate-inferring-sensitive-information-3wyukcsvc6.pdf

Playing Devil's Advocate: Inferring Sensitive Information from Anonymized Network Traces.

https://www.ida.liu.se/labs/rtslab/publications/2007/BurbeckNadjm07.pdf

Adaptive real-time anomaly detection with incremental clustering

Background Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. Objectives To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. Methods We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). Results For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. Discussion and conclusion This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.

Spot the difference: comparing results of analyses from real patient data and synthetic derivatives

Threat intelligence helps businesses and organisations make the right decisions in their fight against cyber threats, and strategically design their digital defences for an optimised and up-to-date security situation. Combined with advanced security analysis, threat intelligence helps reduce the time between the detection of an attack and its containment. This is achieved by continuously providing information, accompanied by data, on existing and emerging cyber threats and vulnerabilities affecting corporate networks. This paper addresses challenges that organisations are bound to face when they decide to invest in effective and interoperable cybersecurity information sharing and categorises them in a layered model. Based on this, it provides an evaluation of existing sources that share cybersecurity information. The aim of this research is to help organisations improve their cyber threat information exchange capabilities, to enhance their security posture and be more prepared against emerging threats.

https://www.mdpi.com/2073-431X/9/1/18/pdf

Interoperability Challenges in the Cybersecurity Information Sharing Ecosystem

Balancing the needs of a data analyst with the privacy needs of a data provider is a key issue when data is sanitized. We treat both the requirements of the analyst and the privacy expectations as policies, and compose the two policies to detect conflicts. The result can be applied to an intermediate data representation to sanitize the relevant pans of the data. We conclude that this method has promise, but more work is needed to determine its effectiveness and limits.

How to sanitize data

This work explores issues of computational disclosure control. We examine assumptions in the foundations of traditional problem statements and abstract models. We offer a comprehensive framework, based on the notion of an inference game, that unifies various inference problems by parameterizing their problem spaces. This work raises questions regarding the significance of intractability results. We analyze common structural aspects of inference problems via case studies; these emphasize why explicit policies are needed to specify all social context and ethical values relevant to a problem instance.

Sanitization models and their limitations

The problem of removing sensitive information from data before it is released publicly, or turned over to less trusted analysts, underlies much of the unwillingness to share data. The solution is to sanitize, or deidentify, parts of the data. When dealing with network addresses, the set of available addresses is finite. This limits some aspects of the sanitization. We analyze this problem in detail, and suggest approaches to ameliorate it.

/pdf/some-problems-in-sanitizing-network-data-js419qeihi.pdf

Some Problems in Sanitizing Network Data

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How to sanitize data

Sanitization models and their limitations

Some Problems in Sanitizing Network Data