Ramblings of a compulsive engineer

Our first post on Linked Closed Data missed some important factors when we argued the inevitability of closed Linked Data publishing, namely — as the title of this post implies — privacy and confidentiality.

The need to provide access to sensitive data while maintaining confidentiality will be a major motivation for Closed Linked Data publishing. Rather than adopt a second format for publishing sensitive data, publishers will be keen to re-use existing Linked Data publishing infrastructure. The Linked Data community needs to converge on standards and develop implementations to support this as soon as possible.

Chris Gutteridge highlighted this in a post on the institutional data of the University of Southampton. For a university there are a number of uses a student might have for their own personal data, data which is confidential and thus cannot be published publicly. 

Chris points out that in this domain there are also some complicated issues regarding student sponsors which might arise if certain assesment data was available electronically. These issues are of course not specific to Linked Data publishing, but it is good to know that people give these issues thought.

 

To consider how reveue models for Linked Data might work, it is helpful to consider how Linked Data fits into the classification as an economic good. To begin with we will consider the simplest case, Linked Open Data where the dataset has been declared public domain.

• Non-rivalrous
  Information, and thus Linked Data, is a non-rival good; a good which can be enjoyed simultaniously by any number of consumers (ignoring technological limitations such as network bandwidth and processing power).
• Durable
  Informational goods are also durable; one person's use of a piece of information does not expend that resource and subsequently prevent any others from using it.
• Non-excludable
  A Linked Open Dataset which has no restriction on access is a non-excludable good; it is not possible to prevent people who have not paid for it from enjoying access to it.
• Intangiable
  Information goods are generally all intangiable goods, good which are themselves not physical objects. Intangiable goods are commonly also nonrival and non-excludable goods.

Goods which are both non-rivalrous and non-excludable are classed as 'public goods' in economic terms. Goods which are both rival and excludable, which are the more common sort of good, are known as 'private goods'.

Public goods are understood to be difficult to charge for directly, as the non-excludability prevents payment for access revenue models. Indeed, economists believe that markets are neither a practical or efficient means of allocating pure public goods.

Naturally, producers and sellers of public goods have a vested interest in ensuring their continued income. Historically, technology and legislation have been the methods used to achieve this; attempting to make what was a public good into something which behaves more like a private good, by making it rival and/or excludable. Digital Rights Management software and copyright law are examples of these technololical and legal methods.

Alternatively, content holders may seek revenue through other means, to offset the impact of freeloading. Advertising is perhaps the most common method, whereby paid adverts are placed alongside or sometimes integrated with the content. Sponsorship is another method, where costs are covered by from investment from another party which does not seek advertising in return, for example, government funding.

This post elaborates on our arguments on the economic nature of Linked Data, from our paper on Linked Closed Data which we recently posted about.

The use of Linked Open Data is becoming increasingly widespread, boosted by recent moves to increase government transparency and efficiency by publishing non-sensitive datasets for free online. There is now a large 'cloud' of interlinked datasets, as evidenced by efforts to catalogue and visualise the Web of Linked Data.

Content owners governments and research institutions are in a unique position; they have the means to invest in the creation of datasets, yet none of the financial pressures of private companies which require them to turn a profit from such investments. So far, all datasets published as Linked Data have been published for free, without access restrictions. However as Linked Data technology moves beyond the Research and Development stage, and is incorporated into commercial products and services, pressures to generate return on investments will increase. In the face of those pressures it is inevitable that some will seek to monetize Linked Data.

In response to these pressures we can expect to see the rise of Linked Closed Data, datasets which are linked in adherence to Linked Data principles, but to which access or some content is restricted to paying members. It may be possible to meet these financial pressures through other means, such as advertising, however we are sceptical of this (this will be the subject of a later post).

Linked Closed Data will not mean the end of the Web of open Data; closed datasets are unlikely to displace the free alternatives, as commercial datasets are sold on their quality and depth, something which free datasets do not generally assure. It will however enable a market for high quality Semantic data, which may benefit to both companies and consumers.

My colleagues and I recently submitted a paper discussing this subject to the Consuming Linked Data workshop (COLD2010), which unfortunately was not accepted. This post explores our ideas about Linked Closed Data from the paper.

We argued previously that there is a need for a system of identity for Semantic Web Agents, particularly in the process of making judgements of trust. Examining the requirements of a system of identity, we recognise that such a system cannot count on universal uptake among Semantic Web agents, and therefore it cannot require each agent to state an identity for itself. Additionally even if universal uptake could be relied upon, we cannot count on the honest and benevolent behaviour of every Semantic Web agent. Thus, as we briefly mentioned at the end of our previous post, a system of identity for the Semantic Web must be primarily built around observable characteristics as a measure of identity. As an analogy; when surfing the Web you would not rely on a Website's claim that it is your bank's online portal, you would rely on the factors you can observe (such as the domain name and also the digital certificate) to inform your judgement. Digital certificates are especially important if you are connected to the Internet over an untrusted network connection. Building on our earlier example of a rudimentary HTTP-based Semantic Web agent, suppose we request a URI from it, and receive some RDF in response. The data we collect about the identity of the agent may look something like the following: Suppose at some later date we again communicate with the agent at the domain agent.example.com, and in the process observe that the DNS entry has changed, and the domain now refers to a new IP address. Do we then consider this to be the same agent which we have previous experience of? Further, is the information we have sufficient to make such a decision? Other attributes may influence the judgement of similarity if they significantly alter the behaviour of the agent, software version numbers or digital certificates, for example. Returning to our analogy, if your browser stored the credentials for your bank's online banking portal, you would specify very strict criteria, very similar to what we described above, to dictate which websites are permitted to see this information. Below follows a second observation record, for an interaction with the same agent at a different IP address. It is possible to encode our criteria for equivalence using OWL (to some degree) such that a reasoner can identify that two agents are in fact the same entity. This involves declaring a class of all things which meet the criteria of being a particular agent such that those which meet the necessary and sufficient criteria may be considered the same. Unfortunately the equivalence afforded by OWL causes the effective merging of the identifiers, such that, as below, the metadata from the two different requests becomes inseparable. The problem with this approach is not the use of OWL classification (though it is somewhat ill suited to this task), rather it is the result of a simplistic ontology design. We acknowledge that this crude example ontology has many flaws (the assumption that a HTTP agent operates on a sole port and network address, for example), however to fully satisfy our potential requirements we must adopt an event-based ontology design, as these observations are inherently temporal in nature.

Open Data movements are gradually gaining traction; government transparency efforts in the US and the UK have begun to release data-sets, some of which are published in Linked Data form. As the range and variety of Semantic Web data publishers grows, it is increasingly important that we address the problem of trust. Previously we discussed the challenges of a trust layer for the Semantic Web, and more recently, how we think these challenges should be faced. We are convinced that provenance and reputation information will be a crucial basis for Semantic Web trust decisions. Reputation and provenance are by no means new subjects in the domain of Computer Science, both are grounded in substantial bodies of literature. Existing techniques will likely require some adaption in order to match the challenges of the Web of Linked Data. Hartig and Zhao's provenance vocabulary for Linked Data does exactly this, taking existing provenance techniques in a Web-friendly direction, recognising the distinctions between data curation, publishing and access. To do similar for reputation mechanisms will not be prohibitively difficult, however there remains a missing piece of the technological puzzle: a system of identity. A notion of identity is necessary for any judgement of trust in order to fully link together available information. The FOAF vocabulary gives us identifiers for people, and the FOAF+SSL proposals allow us to prove the ownership of (Web of Trust, or PKI style) digital certificates, however there is of yet no accepted means of identifying a Semantic Web software agent (e.g. a Webserver) beyond the foaf:Agent type. In order to properly describe the identity of a Semantic Web agent we require more information than a single URI. For example, in the case of a HTTP-Based Semantic Web agent (a Webserver), metadata such as the hostname and network port is to some purposes integral to the identity of the agent. To avoid coining a new identity with every HTTP request we must have some criteria by which we judge that the other parties of different data exchanges are the same entity. An important point to make here is that we cannot rely on declarative identities, that is we cannot count on universal uptake among Semantic Web agents of a vocabulary in which to assert identity. Thus an appropriate identity mechanism must consider both observational identities (identities coined by another agent based on its observations) and declarative identities.

One of the issues which my internal examiner raised with my interim report was that while I described the differing definitions of trust in the field, I failed to describe the definition I was adopting for my work. This post attempts to describe my definition of trust, in the range of contexts in which it is used. Depending on the context in which it is used, the term trust may identify a number of different forms of trust, and the distinction between them is rarely made. We describe our definition for each of these below.

Trust as an act

We consider this to be the primary meaning of the term "trust". Trusting is the act of relying on the behaviour of another individual in an uncertain environment, where it is subjectively perceived that the outcome of the situation is contingent on the behaviour of the other individual. Morton Deutsch's definition of trust is perhaps the most widely accepted, it states that:

1. An individual is confronted with an ambiguous path, a path that can lead to an event perceived to be beneficial (Va⁺) or to an event perceived to be harmful (Va⁻);
2. they perceives that the occurrence of Va⁺ or Va⁻ is contingent on the behaviour of another person; and
3. he perceives the strength of Va⁻ to be greater than the strength of Va⁺.

If he chooses to take an ambiguous path with such properties, I shall say he makes a trusting choice; if he chooses not to take the path, he makes a distrustful choice.

We differ in opinion with Deutsch on two counts; we don't consider it necessary for Va⁻ to be harmful, only that it be less preferable than Va⁺, and thus also that the relative strengths of Va⁺ and Va⁺ need not be a factor in whether it is labeled a trusting choice or not. Reference information for Deutsch's work can be found on Google Scholar and the above passage is reproduced from Marsh's PhD Thesis on trust as a computational concept. As an aside, we do not believe that one can trust in an inanimate object, the true target of trust must be elsewhere. To trust in the strength of a tree branch is instead to trust that ones own internal models and estimates of its strength are correct. To trust in a safety harness is a similar situation, one does not trust the harness itself, instead one trusts first ones own personal judgement that the safety harness appears safe and then that those who are responsible for constructing and maintaining the harnesses have done so with due care and diligence.

Trust as a decision

The decision of whether or not to trust is a choice between different courses of action, of which one or more is a trusting path, and one or more is a path which does not rely on trust. When dealing with complex, multifaceted decisions, potential paths may include measures to decrease the degree of risks or selectively avoid particularly risky events, thus it is often possible to take a trusting path which does not rely on trust in every respect. The degree of risk, the stakes, and the utility of potential outcomes may all play a role in the decision of whether to trust, however one must remember that their evaluation and weighting are inherently subjective.

Trust as a bond

Trust as a bond between two people is the notion that they are able to comfortably rely on the behaviour of each other. Thus a bond of trust is the confidence that each will act in the best interests of the other when placed in a scenario where the utility of the other is contingent on their own actions.

Trust as a property of society

Trust within society arises from the confidence that other members of the society share the same core values and ideals as oneself, and the conjecture that they will therefore behave in a manner which is consistent with these. These behavioral expectations — or social norms — are enforced within the group and breaching them can lead to punishment and exclusion. For an extensive discussion of the roles trust plays within society, see O'Hara's book "Trust: from Socrates to Spin".

Previously we explored the challenges of trust on the Semantic Web and described our take on how we might go about engineering a trust layer for the Semantic Web technology stack. This post elaborates on the challenge of making a judgement of trust. Recalling the two questions posed in the previous post:

1. Can I rely on this piece of information?
2. Can I trust this service provider?

As we observed previously, both questions call for a judgement to be made based on available information.

Consider the first question, of whether to rely on — and therefore trust in — a piece of information. We believe this decision should be based on the level of belief that is held in that statement. Furthermore, our level of belief in a statement should be grounded in an assessment of its credibility and plausibility.

To clarify further, we consider the credibility of a statement to be an assessment of the reliability and trustworthiness of the agents and processes involved in its assertion. Such an assessment would likely include analysis of the provenance data associated with the statement, as well as a review of reputation information and first-hand experiences of the actors and processes involved.

With respect to plausibility, we consider it to be a measure of how likely a statement is to be true, against the background of our existing knowledge, taking into account confirmatory or contradictory knowledge and trends.

The second question has much in common with the first; while the primary concern of the judgement is over the expected behaviour of the service provider, it too must be concerned to some degree with the provenance of information.

Reputation information is valuable in judging expected behaviour and facilitates interactions with yet un-encountered providers, however the provenance of reputation information is also important because disreputable sources may provide fraudulent information when collaborating with disreputable service providers.

Therefore, if we are to construct an ecosystem of Semantic Web technologies in order to engineer trust as a macro phenomena, we must first engineer robust provenance and reputation systems for the Semantic Web.

Trust has long been foreseen as challenge for the Semantic Web research community, appearing in the upper echelons of the Semantic Web Layer Cake technology stack, however Semantic Web research around the topic of trust does not seem to have a clear idea of what exactly this challenge is. Jen Golbeck's prominent work with Semantic Web technologies has harnessed trust within social networks, putting it to tasks such as Email filtering and film recommendation, unfortunately this does not really shed any light on the role trust might play in the Semantic Web technology stack. If we unpack our expectations of a Semantic Web trust layer, taking the time to consider what we expect it to achieve,  by what questions we wish to be able to ask of it, we generally arrive at two questions:

1. Can I rely on this piece of information?
2. Can I trust this service provider?

These two questions are fundamentally different; the first pertains to the truth of a piece of information, whereas the second concerns the probable behaviour of another agent. However both are similar in that they require a judgement to be made based on information such as provenance and reputation. To construct a trust layer we require both the capacity to make such judgements and the information on which to ground such decisions, both of which represent sizable research challenges. The Semantic Web trust layer will not be a single technology, rather a collection of interacting techniques and standards whose emergent macro phenomena we must engineer to be trust.

Contrary to my previous post, I'm back on a self-hosted wordpress weblog. The main reason for the change is that Google are discontinuing support for (s)FTP publishing in late March. The transition over to Wordpress was painless (though I did have to switch away from FTP publishing to make the posts importer work properly) and the admin interface has grown up a lot since I last installed it, I'm very impressed with how clean and efficient it's become. I've settled on this minimal theme for now, but I intend to make it my own over the next few months. In the mean time, you can hopefully expect more frequent updates as I intend to make this blog a staging area of sorts for my research, a means of clarifying and refining my thoughts.

In contrast to my normal coding related posts, this one is a status update on what I've been up to in the time since the end of my undergraduate degree.

Summer 2008 saw the conclusion of my undergraduate Masters degree in Computer Science, four years of both challenging and interesting work, and a result I am proud of every time it crosses my mind.

Over the summer I undertook another internship with the ALADDIN project; blending HTML, CSS, Java, Javascript and a drop or two of PHP I converted the desktop application I built the previous summer into a cross platform web application. Situational Awareness visualises publicly available weather sensor data in real-time, and is available online.

Come October I started a PhD on Trust and the Semantic Web, supervised by Nicholas Gibbins. So far it's been very interesting, and also in hindsight a very wise decision, given the current economic climate. I'm starting to see where my research is going now and how it fits into the wider picture of the Semantic Web and I'm due to start writing my 9 month progress report soon, which should help crystallise my ideas further.

In my spare time I've joined the Southampton Open Wireless Network society (SOWN) who lend out custom Wireless Access points to students around Southampton, allowing students to share their Wifi with members of the community. It's been a lot of fun, a lot to learn and also a great outlet for any coding urges that I might have.

SOWN have some big news in the pipeline, and it's exciting to see it coming together. We hope to have some media coverage of it closer to the time, so if you're in the Southampton area you probably won't miss it!