Controlled P2P RDF Metadata Distribution using Semantic Digital ...

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Controlled P2P RDF Metadata Distribution using Semantic Digital Rights Management Roberto García1, Giovanni Tummarello2 1

Universitat de Lleida

Jaume II, 69. E-25001 Lleida, Spain Fax. +34 973 702 702 [email protected] 2

Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland [email protected]

Abstract. Since the early works in the W3C Semantic Web initiative, RDF has been generically indicated as a potential basis for legally binding exchange of semantically structured information. In this paper we introduce and detail a procedural framework that could support such legally binding exchange. The proposed methodology is based on a Copyright Ontology, a copyright conceptualisation which includes concrete rights expression languages like MPEG-21 REL, and an RDF model decomposition based on the Minimum Self Contained Graph theory. The procedure seems particularly useful when applied to P2P semantic metadata diffusion scenarios.

Keywords: peer to peer, metadata, diffusion, copyright, digital rights management.

1.

Introduction

The knowledge representation capabilities of RDF are agnostic with respect to the content and the purpose for which it is used. Since the early works in the W3C Semantic Web initiative, however, a few use cases stood out and among these there was the idea [1] that RDF might have been potential basis for legally binding exchange of semantically structured information. In this paper we relate to this use case by addressing a scenario in which information does not simply go directly from the source to the intended destination. Information is instead mashed up, aggregated, filtered, republished, annotated possibly by several entities before being consumed by a human or a machine agent. Such scenario is in fact one of the backbones of what

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has been defined “Web 2.0” and it happens daily, e.g. when web content is negotiated, possibly multiple consecutive times, using RSS feeds. This scenario, and the original motivation for this work, comes from the works on the DBin semantic Web platform [2]. DBin is an application which enables users to browse and create semantically structured information which is then exchanged in P2P fashion with other users. By nature, DBin proposes a model where information is widely and freely replicated: each client stores the information that is received and will simply serve it out to those who ask for it. While such open information exchange is in general fascinating, it is clearly not suitable to address many important use cases where data producers would disagree with uncontrolled use and redistribution of their content. For example, a stock price web service might be willing to provide real time information to a subscriber as long as “it is not publicly redistributed before 10 minutes”. Similarly, in a DBin P2P RDF group, a user might want to give information to other peers “as long as it is redistributed only to those who have a verified ‘@deri.org’ e-mail address”. In one such information exchange scenario, a simple access control to the information sources, e.g. password protected, does not help. Similarly a non machine readable licence, e.g. a fixed licence that one has to agree with an “I understand the terms and conditions” checkbox at sign up time, would not allow any automatic and dynamic handling of such information distribution. In this paper, we therefore address the need for controlled RDF information exchange in a P2P environment; the procedure that we present enables a source peer (from here on source) to provide a piece of RDF to a receiving peer (receiver) subject to the digital signing of an agreement which covers specifically the information that is about to be transferred without, of course, reporting the information itself.

1.1.

Legal protection for a database: a background

In this work we address this scenario by a combination of digital signature technologies on information derived hashes and an ontology to describe data exchange to craft and verify agreements. The ontology, described in Section 3, is referred to as the Copyright Ontology as it was developed originally to assist in issues related to Digital Rights Management (DRM). The

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ontology will prove to be ideal for describing several kinds of exchange information policies and therefore support our scenario. It must however be clarified that this ontology is used for its expressive ability and not to rely on Copyright laws to protect the content itself. Copyright laws are in fact not well suitable for the protection of pieces of data which by themselves have no artistic value. Copyright protections on database is granted under certain conditions [3]; under U.S. Laws for example databases are protected only when some degree of creativity is involved, that is when they can be considered a “collection and assembling of preexisting materials or of data that are selected in such a way that the resulting work as a whole constitutes an original work of authorship” [4]. In any case, however, there is no copyright protection on the single bits of database information, unless they are themselves works of creativity, no matter which process was involved in gathering the single bits.Given such terms of the Copyright law, database owners must instead use contract law to obtain protection. This typically happens in form of a database usage licence in which the terms and conditions are specified. One such contract is therefore required to back up the technical procedure described in this paper. And the Copyright Ontology provides de required means to model machinereadable versions of them. Once such agreement is in place, the procedure we describe will make it verifiable and therefore enforceable.

2.

The proposed exchange procedure: outline

In this section we describe the procedure by which the source provides RDF to the receiver along with a licence which specifies how such information may be used. The procedure has one principal requirement: that the identity of the remote party can be trusted. i.e. that the parties know or have a way to track the legal identity of the creator of the public key that will verify the signing of the licences. There are many ways by which this can be achieved, e.g. via a certifying third party like Verisign, so the discussion of these is outside the scope of this paper. For the rest of the discussion we will use the term cite to indicate a pointer to the information, e.g. an URL. With the term quote we indicate providing the information itself along with additional control information.

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In time steps, the exchange proceeds as follows, being R a S the involved parties: 1) R makes a request to S. As a result of such request, R expects S to give information expressed in RDF. Optionally: It might be desirable that the request be itself digitally signed to certify its provenance. This could be useful to avoid disclosing indirect information (e.g. for example the hash itself of the information) to malicious third parties. 2) S receives the request. S creates the RDF for the answer and uses the minimum self contained graph (MSG) decomposition as highlighted in the next section to obtain a set of digital hashes which enable to describe the information without disclosing it: R will be able to check that it refers to the information just when it obtains the information itself. S uses the hashes in a contract created with the methodology described in Section 3 and sends the result, from here on called proposal, to R. Optionally: S signs the proposal so to provide R with the guarantee that if agreed, the answer will actually be provided within the specified terms. In this way, for example, R can later produce the signed request to justify why a certain behaviour was then taken R (e.g. no further disclosure of the information). 3) R receives the proposal and, if it decides that the terms are agreeable, R signs and returns it to S. Optionally: thanks to the properties of MSGs as discussed in the next section, R can check if the answer correspond to information which is already locally known. In this case R could drop the request as not interesting, or proceed, e.g., in case it is important for R to prove that the information was in fact legally acquired. 4) S receives the signed proposal, S stores it and replies with the answer computed in 2). Optionally: the signed proposal might be countersigned by S and given back to R to prove that it was accepted by R. This could be useful for R to show at a later time that, for example, a given piece of information was in fact obtained by legal means.

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2.1.

An introduction to the Minimal Self Contained Graph theory

In this section we will illustrate the Minimum Self Contained Graph (MSG) theory. The discussion will deepen that first illustrated in [5] and will provide the bases for the understanding precisely the procedure. Let's first define what is the minimum “standalone” fragment of an RDF model. As blank nodes are not addressable from outside a graph, they must always be considered together with all surrounding statements, i.e. stored and transferred together with these. MSG are the smallest components of a lossless decomposition of a graph which does not take into account inference such as provided by OWL, as concepts such as RDF-Molecules show [6]. We will here give a formal definition of MSG (minimum Self-contained Graph) and will cite some important properties (for proofs, see [5]): • Def 1. An RDF statement involves a name if it has that name as subject or object. • Def 2. An RDF graph involves a name, if any of its statements involves that name. • Def 3. Given an RDF statement s, the Minimum Self-contained Graph (MSG) containing that statement, written MSG(s), is the set of RDF statements comprised of the statement in question and, recursively, for all the blank nodes involved by statements included in the description so far, the MSG of all the statements involving such blank nodes. It is possible to show however that the choice of the starting statement is arbitrary and this leads to a unique decomposition of the RDF graph into MSGs. It is also possible to prove that: • Theorem 1. If s and t are distinct statements and t belongs to MSG(s), then MSG(t) = MSG(s). • Theorem 2. Each statement belongs to one and only one MSG. • Corollary 1. An RDF model has a unique decomposition in MSGs. This is a consequence of theorem 2 and of the determinism of the procedure. As a consequence of the Corollary 1, a graph can be incrementally transferred between parties by decomposition into MSGs and transfers with granularity down to one MSG at a time. Such transfer would be, as consequence of Theorem 2, maximally network efficient as statements would never be repeated. • Definition 4. The RDF Neighbourhood (RDFN) of a resource is the graph composed by all the MSGs involving the resource itself.

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2.2.

Content based identifiers for MSGs

MSGs are standalone RDF graphs. As such, they can be processed with algorithms like canonical serialization [7]. We use an implementation of this algorithm, part of the RDFContextTools Java library [8], to obtain a canonical string representing the MSG. This string is then hashed to a number of bits appropriate to reasonably avoid collisions (160 bits can be shown to be enough for most practical uses). This hash acts as a unique identifier for the MSG with the fundamental property of being content based, which implies that two remote peers would derive the same ID for the same MSG in their DB. Sets of such IDs are used to identify the information covered in the contracts based on the Copyright Ontology, which is detailed in the next section.

3.

Semantic Digital Rights Management

As it has been previously introduced in Section 1.1, copyright law can provide the required legal support for controlled metadata diffusion under specific circumstances. Copyright terms can be used in order to build licenses for controlled diffusion of metadata.Such licenses are managed by Digital Rights Management (DRM) systems [9], which strive to enable controlled distribution of content and associated usage rights making use of IT components and services along with corresponding law, policies and business models. DRM has been in place for a long time; ranging from CD protection measures to enterprise CMS with access control measures. However, it has been seriously challenged by the interoperability requirements posed by the Internet and worldwide copyright legal systems. One of the main initiatives for DRM interoperability is the ISO/IEC MPEG-21 standardisation effort. The main interoperability facilitation components are the Rights Expression Language (REL), which is based on a XML grammar, and the MPEG-21 Rights Data Dictionary (RDD) which informally defines the terms used in the REL [10]. However, neither the REL nor the RDD define formal semantics like it would be possible to do if they were based on an ontological approach [11]. Consequently, due to the complexity of the DRM domain and the underlying legal issues, DRM interoperability initiatives based on a purely syntactic approach face expressiveness limitations, for instance they are not able to

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capture the underlying rights frameworks [12], and implementation difficulties due to the complexity of their specifications [13]. The limitations of a purely syntactic approach and the lack of formal semantics can be overcome using a semantics based approach based on ontologies [14]. Web ontologies are used in order to benefit from the Semantic Web initiative efforts and facilitate its integration in the Web context. The Copyright Ontology [15], of which we give here an overview, is a conceptualisation effort based on OWL. It allows a more expressive rights expression framework, where the underlying copyright terms are captured. Moreover, it facilitates implementation based on existing Semantic Web tools like reasoners or rule engines. The Copyright Ontology conceptualisation task has been divided in three parts. The conceptualisation starts from building a model for the more primitive part, the Creation Model. Then, the following step is to build the Rights Model, and, finally, the Action Model on the roots of the two previous ones. This section just sketches the main points of these three models. For more details, see [16]. The Creation Model defines the different forms a creation can take. These can be classified on the three top categories common in many upper ontologies [17]: • Abstract: Work, e.g. “Victor Hugo’s Les Misérables”. • Object: Manifestation, e.g. “an edition of Les Misérables”, Fixation, e.g. “a motion picture of Les Misérables”, or Instance, e.g. “a DVD of Les Misérables”. • Process: Performance, e.g. “a scenic or screen play of Les Misérables”, or Communication, e.g. “a broadcast or Internet streaming of the film Les Misérables”. The Rights Model follows the World Intellectual Property Organisation (WIPO) recommendations for worldwide copyright. The more relevant rights from the DRM point of view are the economic rights as they are related to productive and commercial aspects of copyright. The Action Model corresponds to the primitive actions that can be performed on the concepts defined in the Creation Model and which are regulated by the rights in the Right Model. For instance, for the economic rights, these are the actions governed by them: • Reproduction Right: reproduce, commonly speaking copy. • Distribution Right: distribute. More specifically sell, rent and lend.

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• Public Performance Right: perform; it is regulated by copyright when it is a public performance and not a private one. • Fixation Right: fix, or record. • Communication Right: communicate when the subject is an object or retransmit when communicating a performance or previous communication, e.g. a re-broadcast. Other related actions, which depend on the intended audience, are broadcast or make available. • Transformation Right: derive. Some specialisations are adapt or translate. The action concepts are complemented with a set of relations that link them to the action participants. The relations are adopted from the linguistics field and they are based on case roles [18]. The previously introduced pool of primitive actions can be combined in order to build different value chains in the copyright domain. It is complemented with a set of axioms that restrict the ways actions, rights and creation types are related.

3.1.

Semantic DRM in the P2P Diffusion Domain

If we try to model the P2P RDF metadata diffusion scenario using the Copyright Ontology, it is governed by the Reproduction and Communication Rights. The Reproduction Right governs the Copy action that reproduces a piece of metadata from Peer A, where the piece resides originally, to Peer B, where the piece also resides when the copy is completed. The Communication Right governs the generic action Communicate. This action corresponds, among others, to the situation where the agent responsible for a peer makes content available to others from the place and time individually chosen by them. Therefore, in the context of P2P diffusion, this is the right required by a peer in order to make a piece of metadata available for others to copy. These rights provide the basic terms for the controlled metadata diffusion and are complemented with the contract building primitives [19]. These primitives are also provided by the Copyright Ontology and constitute the means to build specific compromises among parties. The basic building blocks for contracts are based on event patterns, i.e. a pattern of actions specifying who can perform it, what might be affected, when, etc. They are used in order to state what is obliged, permitted or prohibited by a contract. They are naturally captured by the

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ontology terms described so far, i.e. the proposed actions and the case roles are used to model event patterns in the copyright domain. For instance, on the center of Fig. 1 there is a pattern for all Copy events in a Peer to Peer network performed by agent “granted” who copies “content01” from “PeerA” to two peers from the set “PeerB, PeerC, PeerD” at any time point six months after “2007-06-01”. As it has been detailed in Section 2.2, the pieces of data, i.e. MSGs, that are the theme of the actions governed by a contract are specified using a hash. Patterns are combined with a set of terms, detailed in the next paragraphs, that state the modality of these event patterns in copyright contracts, i.e. the pattern is permitted, obliged or prohibited. Permissions are captured by the action Agree. The permitted pattern is linked using the theme case role, whose semantics are to point to the object of an action. Following with the previous example, the agreement between “granter” and “granted” in the upper part of Fig. 1 authorises the pattern pointed by the theme case role, the previous P2P copy pattern at the centre of the figure. Obligations are modelled as event patterns that must be satisfied at some time point after the event pattern that triggers the obligation is exercised. They are represented using the consequence case role that links the triggering pattern to the one that is obliged. For instance, in the bottom part of Fig. 1 it is stated that, if the copy action is exercised, the consequence is that the “granted” agent must transfer 3 Euros to the “granter” agent before 24 hours from the copy action. On the other hand, if the obligation must be satisfied a priori, i.e. like a precondition, then the condition case role is used. Finally, prohibitions are captured by another action, Disagree. Like for the Agree action, the theme case role is used to link it to the object of the action, in this case to the pattern that is prohibited. For instance, in the previous scenario, the contract might also state that it is forbidden that the “granted” agent changes “content01” using a Disagree pattern with the corresponding Transform action pattern as its theme.

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Fig. 1. Agreement that authorises copy actions in a P2P scenario (event pattern) an whose consequence is an economic transfer (obligation)

4.

Implementation

MSG theory and tools has been implemented in [¡Error! Marcador no definido.] based on the Jena and the Sesame toolkits. The entire procedure as described in this paper is covered in the implementation of an upcoming version of the DBin platform [¡Error! Marcador no definido.] but it will be made available as a standalone library to be used embedded in other applications which exchange RDF. On the other hand, the Copyright Ontology has been implemented using the Description Logic variant of the Web Ontology Language. The motivation of this choice is to enable a straightforward and efficient implementation of license checking based on an extensive use subsumption and DL (Description Logic) reasoners to compute it. More concretely, Pellet20 has been selected because it can reason over custom data types and this has been very useful to check contract time ranges. Contracts event patterns are modelled as OWL Classes and intended uses are modelled as instances. In order to check if a use (instance) is authorised by a set of licenses (classes) a DL

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reasoner is used to classify the instance in the available classes for event pattern. If the instance is classified into a class that is the subject of an agreement, i.e. the theme of an Agree, the use is authorised. Suppose, for example, that we want to model a contract that allows the agent "granted" to copy the metadata "fragment01" from "peerA" to either "peerB", "peerC" or "peerD". Additional restrictions are that at most it can be simultaneously copied to 2 peers (as a result of an individual copy action) and that the copy can be performed from June 1st 2007 to December 1st 2007. Table 1 shows the class pattern for the theme value of the contract Agree. The pattern is for Copy actions, so it is a subclass of Copy, and it is equivalent to the class resulting from the intersection of four OWL restrictions, which constitute the necessary and sufficient conditions that would trigger the classification of authorised usage instances. Table 1. Class pattern for the actions authorised by the example license Pattern ⊑

Copy

(1)

Pattern ≡

∀pointInTime.≥ 2006-01-01T00:00:00, ≤ 2006-06-30T23:59:59 ⊓

(2)

∃agent.{granted} ⊓ ∃origin.{peerA} ⊓ ∃theme.{fragment0001} ⊓

(3)

( ≤ 2 recipient ) ⊓

(4)

∀recipient.{peerC, peerD, peerB}

(5)

The previous event pattern is the theme of an Agree, so it can be inferred that the any action classified as one of its instances is permitted, see Fig. 2. It is also checked that the permitted action, despite it is agreed, is not prohibited. An action is prohibited if it is classified in the pattern pointed by the theme of a Disagree. This additional check is required because we use this behaviour in order to model revocation and to avoid the open world assumption (OWA) inherent to DL reasoners. More details about the OWA issue are available from [21]. Moreover, in some cases, DL expressivity is not enough. Then, it is possible to combine DL reasoners with semantic web rules in order to attain a greater level of expressivity [22].

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Copy Pattern

[ a Copy; agent :granted; theme :fragment01;

origin :peerA; recipient :peerC, :PeerD ]

Agreed

Fig. 2. Authorised use for the contract pattern in Table 1, i.e. classified as instance of the pattern class

5.

Discussion and conclusions

The copyright ontology constitutes a complete framework for representing copyright value chains and the associated flow of rights, situations, agreements, offers, etc. Moreover, it has a straightforward implementation thanks to its formalisations as an OWL ontology with can be used by Description Logic reasoners in order to check if a particular action is authorised by a set of licenses. This general framework can be specialised and used in conjunction with the Minimum Self Contained RDF graph theory to implement a P2P RDF diffusion mechanism which could form a base for legally binding agreements. The proposal has two main features: for one it works based on tools that are typically available or can be simply added in most Semantic Web application. RDF handling and crafting capabilities are used for creating licenses and responses, querying capabilities are used to check for previous licences and all that it takes to check if an action is allowed is OWL-DL inference. Secondly, the proposed methodology uses RDF content based hashes, which enable features otherwise not possible such as, e.g., the ability for the receiving party to recognize that the precise piece of information was in fact already known thus aborting the exchange. One could say that the proposed approach would be limited to the case of protection against “verbatim” redistribution of information. While this is the case technically, i.e. MSG IDs would change with any simple modification -e.g., the insertion of a meaningless triple attached to any blank node-, this does not change the validity and applicability of the procedure. It is in fact

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long established that copyright laws protect not only the exact representation of the protected work but also derived representations. The case is similar to one licensing a photo from a collection, changing a single pixel and wanting to redistribute it as one’s own production outside fair use limits. In conclusion, in this work we address a scenario inspired by the need for information diffusion control in a P2P RDF based information management platform, in a way which is general and domain independent. Independently from the implementing platform, we believe that this work might be effective to bring the Semantic Web a step closer to users with non public domain datasets.

5.1.

Related Work

While we consider DRM a natural approach for the purpose of this paper, there are several generic policy systems based on Semantic Web tools. Ontology-based approaches rely on the expressive capabilities of Description Logic languages, such as OWL. DL reasoners can be then used to classify policies and contexts and enable deductive inferences for policy checking. This is the approach for the Copyright Ontology implementation presented in this paper. A generic policy language also following this approach is KAoS [23] which can reason about licenses by ontological subsumption. KAoS requires however OWL-Full reasoning capabilities and its implementation is based on a theorem prover. In contrast, rule-based approaches take the perspective of Logic Programming to encode policies as rules with variables. Rei is a policy framework based on rules [24]. Rules are expressed as triples following a pattern that is typical of logical languages like Prolog. Rei overcomes the variables limitation and enables the definition of policies that refer to dynamically determined values. However, this prevents it from exploiting the full potential of the OWL language. In fact, Rei rules knowledge is treated separately from OWL ontology knowledge due to its different syntactical form. To overcome the limitations of this trade-off between ontology and rule-based policies, hybrid solutions have also been proposed [25]. This is also the choice for the Copyright Ontology implementation, in fact, semantic web rules are used in order to model rights flow, rights exceptions or complex contracts [22,¡Error! Marcador no definido.].

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