Luke Hutton

Cleaning up the masses: Exclusion lists to reduce contamination with HPLC-MS/MS

Mass spectrometry, in the past five years, has increased in speed, accuracy and use. With the ability of the mass spectrometers to identify increasing numbers of proteins the identification of undesirable peptides (those not from the protein sample) has also increased. Most undesirable contaminants originate in the laboratory and come from either the user (e.g. keratin from hair and skin), or from reagents (e.g. trypsin), that are required to prepare samples for analysis. We found that a significant amount of MS instrument time was spent sequencing peptides from abundant contaminant proteins. While completely eliminating non-specific protein contamination is not feasible, it is possible to reduce the sequencing of these contaminants. For example, exclusion lists can provide a list of masses that can be used to instruct the mass spectrometer to ‘ignore’ the undesired contaminant peptides in the list. We empirically generated be-spoke exclusion lists for several model organisms (Homo sapiens, Caenorhabditis elegans, Saccharomyces cerevisiae and Xenopus laevis), utilising information from over 500 mass spectrometry runs and cumulative analysis of these data. Here we show that by employing these empirically generated lists, it was possible to reduce the time spent analysing contaminating peptides in a given sample thereby facilitating more efficient data acquisition and analysis. Biological significance Given the current efficacy of the Mass Spectrometry instrumentation, the utilisation of data from ~500 mass spec runs to generate be-spoke exclusion lists and optimise data acquisition is the significance of this manuscript. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012].

An architecture for ethical and privacy-sensitive social network experiments

Social Network Sites (SNSs) are used for sharing personal data and delivering personalised services to hundreds of millions of users, and thus represent an important sector of the Digital Economy. Measuring and collecting data from SNSs is crucial for research and development of new services, but the sensitive and personal nature of these data means that great care must be taken by researchers when conducting SNS studies. This paper presents a work-in-progress architecture for conducting experiments across multiple SNSs while acknowledging and preserving participant privacy. We evaluate the architecture by conducting an experiment using live SNS data, exploring willingness to share sensitive data with researchers. We also outline some outstanding challenges as we finalise the implementation of the architecture.

Toward Reproducibility in Online Social Network Research

The challenge of conducting reproducible computational research is acknowledged across myriad disciplines from biology to computer science. In the latter, research leveraging online social networks (OSNs) must deal with a set of complex issues, such as ensuring data can be collected in an appropriate and reproducible manner. Making research reproducible is difficult, and researchers may need suitable incentives, and tools and systems, to do so. In this paper, we explore the state-of-the-art in OSN research reproducibility, and present an architecture to aid reproducibility. We characterize the reproducible OSN research using three main themes: 1) reporting of methods; 2) availability of code; and 3) sharing of research data. We survey 505 papers and assess the extent to which they achieve these reproducibility objectives. While systems-oriented papers are more likely to explain data-handling aspects of their methodology, social science papers are better at describing their participant-handling procedures. We then examine incentives to make research reproducible, by conducting a citation analysis of these papers. We find that sharing data are associated with increased citation count, while sharing method and code does not appear to be. Finally, we introduce our architecture which supports the conduct of reproducible OSN research, which we evaluate by replicating an existing research study.

Beyond the EULA: Improving Consent for Data Mining

Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

Short paper: "here i am, now pay me!": privacy concerns in incentivised location-sharing systems

Social network sites, location-sharing services and, more recently, applications enabling the quantified self, mean that people are generating and sharing more data than ever before. It is important to understand the potential privacy impacts when such personal data are commercialised, to ensure that expectations of privacy are preserved. This paper presents the first user study of incentivised location sharing, where people are given a direct monetary incentive to share their location with a business or their social network. We use Nissenbaums framework of contextual integrity in a preliminary user study (n=22) to investigate potential privacy risks with such services. We find that monetisation changes why people share their data, but not the frequency of disclosures. Our results motivate further study and are useful for designers of location-sharing systems and researchers who wish to leverage the diverse range of personal data that are available in a privacy-sensitive manner.

Logging you, Logging me: A Replicable Study of Privacy and Sharing Behaviour in Groups of Visual Lifeloggers

Low cost digital cameras in smartphones and wearable devices make it easy for people to automatically capture and share images as a visual lifelog. Having been inspired by a US campus based study that explored individual privacy behaviours of visual lifeloggers, we conducted a similar study on a UK campus, however we also focussed on the privacy behaviours of groups of lifeloggers. We argue for the importance of replicability and therefore we built a publicly available toolkit, which includes camera design, study guidelines and source code. Our results show some similar sharing behaviour to the US based study: people tried to preserve the privacy of strangers, but we found fewer bystander reactions despite using a more obvious camera. In contrast, we did not find a reluctance to share images of screens but we did find that images of vices were shared less. Regarding privacy behaviours in groups of lifeloggers, we found that people were more willing to share images of people they were interacting with than of strangers, that lifelogging in groups could change what defines a private space, and that lifelogging groups establish different rules to manage privacy for those inside and outside the group.

Some Challenges for Ethics in Social Network Research

Social network sites (SNSes) comprise one of the most popular networked applications of late, with hundreds of millions of users. Collecting and analysing data from such systems creates myriad ethical issues and challenges for researchers both in networked systems and other fields, as highlighted by recent media sensitivity about research studies that have used data from Facebook. In our workshop contribution we discuss recent work that we have been carrying out in the area of responsible SNS research, revolving around themes of reproducibility, consent, incentives, and creating ethical workflows.

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach

Background The recent proliferation of self-tracking technologies has allowed individuals to generate significant quantities of data about their lifestyle. These data can be used to support health interventions and monitor outcomes. However, these data are often stored and processed by vendors who have commercial motivations, and thus, they may not be treated with the sensitivity with which other medical data are treated. As sensors and apps that enable self-tracking continue to become more sophisticated, the privacy implications become more severe in turn. However, methods for systematically identifying privacy issues in such apps are currently lacking. Objective The objective of our study was to understand how current mass-market apps perform with respect to privacy. We did this by introducing a set of heuristics for evaluating privacy characteristics of self-tracking services. Methods Using our heuristics, we conducted an analysis of 64 popular self-tracking services to determine the extent to which the services satisfy various dimensions of privacy. We then used descriptive statistics and statistical models to explore whether any particular categories of an app perform better than others in terms of privacy. Results We found that the majority of services examined failed to provide users with full access to their own data, did not acquire sufficient consent for the use of the data, or inadequately extended controls over disclosures to third parties. Furthermore, the type of app, in terms of the category of data collected, was not a useful predictor of its privacy. However, we found that apps that collected health-related data (eg, exercise and weight) performed worse for privacy than those designed for other types of self-tracking. Conclusions Our study draws attention to the poor performance of current self-tracking technologies in terms of privacy, motivating the need for standards that can ensure that future self-tracking apps are stronger with respect to upholding users’ privacy. Our heuristic evaluation method supports the retrospective evaluation of privacy in self-tracking apps and can be used as a prescriptive framework to achieve privacy-by-design in future apps.