The 3rd International Workshop on Trust in Cloud Computing (IWTCC 2016)

The 3rd International Workshop on Trust in Cloud Computing

Collocated with 9th IEEE/ACM International Conference on Utility and Cloud Computing

December 6-9, 2016

Tongji University – Shanghai, China

 Call for papers

 Trust is a key requirement for the successful uptake and operation of Utility and Cloud Computing (UCC). Service consumers must have belief that their data is safe and protected and service providers must provide sufficient assurances to satisfy these beliefs. However many challenges need to be overcome before this is possible. A myriad of trust relationships can arise between the different players in the UCC service delivery chain and these need to be understood and described as do the consequential assurance approaches that may be required. Complex, and varying, UCC service delivery environments may require very different and context dependent mechanisms to provide the assurances needed. This workshop aims to explore approaches to the definition and establishment of trust relationships in a dynamic and evolving UCC service environment and to investigate mechanisms, methodologies and technologies to enable the provision of assurances that are needed to fulfil users trust beliefs.

The IWTCC will be held in Shangai, China in December, 2016. It provides a forum for researchers world-wide to present their latest work in privacy, security and trust in the cloud, and to show how this research can be used to enable innovation.


We encourage submissions on theoretical as well as experimental studies related to all aspects of trust and security and privacy in the cloud. Topics of interests include, but are NOT limited to, the following categories:

  • Trust models in UCC.
  • Cryptographic techniques for privacy preservation
  • Identity, Authentication and key management
  • Formal verification for cloud architecture
  • Practical cryptographic protocols for cloud security
  • Intrusion Detection Technologies for cloud contexts
  • SDN Security
  • Provenance and digital forensics
  • Monitoring systems in the cloud
  • Remote attestation mechanisms in clouds
  • Trusted computing technology and clouds
  • Cloud Integrity and Audit
  • Privacy and security in Vehicular cloud
  • Multi-tenancy and trust in cloud computing


Important dates and Submission

  • Paper Submissions Due:                5th August, 2016
  • Notification of Acceptance:           1st September, 2016
  • Early Registration Deadline:         21st September, 2016

Authors are invited to submit papers electronically through Easychair. Manuscripts may not exceed six (6) letter size (8.5 x 11) pages including figures, tables and references using the IEEE format for conference proceedings (print area of 6-1/2 inches (16.51 cm) wide by 8-7/8 inches (22.51 cm) high, two-column format with a 3/8 inch (0.81 cm) space between them, single-spaced 10-point Times fully justified text). Submitted papers must represent original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines will be rejected without review and further action may be taken, including (but not limited to) notifications sent to the heads of the institutions of the authors and sponsors of the conference. Additional pages may be purchased (in some circumstances) subject to approval by the proceedings chair. Authors of accepted papers must guarantee that their papers will be presented at the workshop.

Electronic submissions must be in the form of a readable PDF file. Manuscripts must be received by August 5, 2015.

The submission link :

Organising committees

General chairs

Abir Awad, University of South Wales,UK.

Asma Adnane, University of Derby, UK.

Brian Lee, Athlone Institute of Technology, Ireland.

Technical program committee

Ashiq Anjum, University of Derby, UK.

Rafael Timoteo De Sousa Junior, University of Brasilia, Brazil

Christophe Bidan, Supelec, France.

Siraj Shaikh, Coventry University, UK

Minsi Chen, University of Derby, UK.

Eric Filiol, ESIEA, France.

Sara Kadry Hamouda, Athlone Institute of Technology, Ireland.

Ryan Ko, University of Waikato, New Zealand.

Lu Liu, University of Derby, UK.

Theo Lynn, Dulin City University, Ireland.

Sheikh Mahbub Habib, CASED, Darmstadt, Germany.

Ali Miri, Ryerson University, Canada.

John P. Morrison, University College Cork, Ireland.

Kim Smith, University of Derby, UK.

Maryline Laurent, Telecom SudParis, France.

Guy Gogniat, University of Bretagne-Sud, France.

Bachar El Hassan, Libanese University.

Guillaume Hiet, Supelec, France

Kim-Kwang Raymond Choo, University of South Australia.

Qiang Guan, Los Alamos National Laboratory, USA

Kaniz Fatema, Trinity College Dublin.

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Keynote Talk at Pan European StrategyForum Retail at Crowne Plaza Barcelona Fira Center, Barcelona 22 April 2016

I am just back from presenting a keynote talk on the topic of “Governance of Big Data and Analytics to Gain Value from Location Services in the Retail Sector“. This was a very interesting conference of senior executives from the European Retail Sector. the majority of the keynote speakers were also senior executives from the sector.

My contribution was to provide an overview of some of the governance questions that organisations need to ask themselves in order to gain the answers that will guide them towards an effective use of analytics and, in particular, the use of location services, through the use of the 12 Vs of Big Data, see .

The slides are here   and the video can be found here.


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Information Security Conferences in 2016

Concise publishes a comprehensive list of information security conferences updated on a daily basis – worth checking:

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CYBERSUP’16: Workshop on Cybersecurity Risk and Trust Management across Supply Chains

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The 9th International Workshop on Digital Forensics

WSDF 2016 to be held at ARES conference, 31/08-02/09 2016, in Salzburg, Austria.

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JDFSL Special issue on Cyberharassment Investigation: Advances and Trends.

Please find the CFP here

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Codd’s Twelve Rules

Codd’s 12 Rules (for a relational database product) are (still) frequently informally cited, but their original text turns out to be annoyingly difficult to find. They are reprinted here under the principles of fair use and/or fair dealing and have been extracted, verbatim, from ‘Is your DBMS really relational?’, Computerworld. Oct 14, 1985, Vol. 19, pID1 and ‘Does your DBMS run by the rules?’, Computerworld. Oct 21, 1985, Vol. 19, p49.

Note that whilst these rules were appropriate to their time (mid 1980s), their intent (quash marketing hype at a time when any remotely DBMS-like product would probably claim to be “relational”), and their audience (readers of Computerworld, a popular semi-technical industry magazine), they should be considered superseded by C J Date & Hugh Darwen’s “The Third Manifesto” series of writings on the relational model. See

The 12 rules

By E. F. Codd
October 1985

Twelve rules are cited below as part of a test to determine whether a product that is claimed to be fully relational is actually so. Use of the term “fully relational” in this report is slightly more stringent than in my Turing paper (written in 1981). This is partly because vendors in their ads and manuals have translated the term “minimally relational” to “fully relational” and partly because in this report, we are dealing with relational DBMS and not relational systems in general, which would include mere query-reporting systems.

However, the 12 rules tend to explain why full support of the relational model is in the users’ interest. No new requirements are added to the relational model. A grading scheme is later defined and used to measure the degree of fidelity to the relational model.

First, I define these rules. Although I have defined each rule in earlier papers, I believe this to be the first occurrence of all 12 of them together.

In rules eight through 11, I specify and require four different types of independence aimed at protecting customers’ investments in application programs, terminal activities and training. Rules eight and nine — physical and logical data independence — have been heavily discussed for many years.

Rules 10 and 11 — integrity independence and distribution independence — are aspects of the relational approach that have received inadequate attention to date but are likely to become as important as eight and nine.

These rules are based on a single foundation rule, which I shall call Rule Zero:

Rule Zero.

Rule 0: For any system that is advertised as, or claimed to be, a relational data base management system, that system must be able to manage data bases entirely through its relational capabilities.

This rule must hold whether or not the system supports any non-relational capabilities of managing data. Any DBMS that does not satisfy this Rule Zero is not worth rating as a relational DBMS.

One consequence of this rule: Any system claimed to be a relational DBMS must support data base insert, update and delete at the relational level (multiple-record-at-a-time). Another consequence is the necessity of supporting the information rule and the guaranteed access rule.

“Multiple-record-at-a-time” includes as special cases those situations in which zero or one record is retrieved, inserted, updated or deleted. In other words, a relation (table) may have either zero tuples (rows) or one tuple and still be a valid relation.

Any statement in the manuals of a system claimed to be a relational DBMS that advises users to revert to some nonrelational capabilities “to achieve acceptable performance” — or for any reason other than compatibility with programs written in the past on nonrelational data base systems — should be interpreted as an apology by the vendor. Such a statement indicates the vendor has not done the work necessary for achieving good performance with the relational approach.

What is the danger to buyers and users of a system that is claimed to be a relational DBMS and that fails on Rule Zero?

Buyers and users will expect all the advantages of a truly relational DBMS, and they will fail to get these advantages. Now I shall describe the 12 rules that, together with the nine structural, 18 manipulative and three integrity features of the relational model, determine in specific detail the extent of validity of a vendor’s claim to have a “fully relational DBMS.”

All 12 rules are motivated by Rule Zero defined above, but a DBMS can be more readily checked for compliance with these 12 than with Rule Zero.

The information rule.

Rule 1: All information in a relational data base is represented explicitly at the logical level and in exactly one way — by values in tables.

Even table names, column names and domain names are represented as character strings in some tables. Tables containing such names are normally part of the built-in system catalog. The catalog is accordingly a relational data base itself — one that is dynamic and active and represents the metadata (data describing the rest of the data in the system). The information rule is enforced not only for user productivity but also to make it a reasonably simple job for software vendors to define additional software packages (such as application development aids, expert systems and so on) that interface with relational DBMS and, by definition, are well integrated with the DBMS.

That is, these packages retrieve information already existing in the catalog and, as needed, put new information in the catalog by the very act of using the DBMS.

An additional reason to enforce this rule is to make the data base administrator’s task of maintaining the data base in a state of overall integrity both simpler and more effective. There is nothing more embarrassing to a data base administrator than being asked if his data base contains certain specific information and his replying after a week’s examination of the data base that he does not know.

Guaranteed access rule.

Rule 2: Each and every datum (atomic value) in a relational data base is guaranteed to be logically accessible by resorting to a combination of table name, primary key value and column name.

Clearly, each datum in a relational data base can be accessed in a rich variety — possibly thousands — of logically distinct ways. However, it is important to have at least one way, independent of the specific relational data base, that is guaranteed, because most computer-oriented concepts (such as scanning successive addresses) have been deliberately omitted from the relational model.

Note that the guaranteed access rule represents an associative addressing scheme that is unique to the relational model. The rule does not depend at all on the usual computer-oriented addressing. However, the primary key concept is an essential part of it.

Systematic treatment of null values.

Rule 3: Null values (distinct from the empty character string or a string of blank characters and distinct from zero or any other number) are supported in fully relational DBMS for representing missing information and inapplicable information in a systematic way, independent of data type.

To support data base integrity, it must be possible to specify “nulls not allowed” for each primary key column and for any other columns where the data base administrator considers it an appropriate integrity constraint (for example, certain foreign key columns).

Past techniques entailed defining a special value (peculiar to each column or field) to represent missing information. This would be most unsystematic in a relational data base because users would have to employ different techniques for each column or domain — a difficult task because of the high level of language in use (and a task that I believe would decrease user productivity).

Dynamic on-line catalog based on the relational model.

Rule 4: The data base description is represented at the logical level in the same way as ordinary data, so that authorized users can apply the same relational language to its interrogation as they apply to the regular data.

One consequence of this is that each user (whether an application programmer or end user) needs to learn only one data model — an advantage that nonrelational systems usually do not offer (IBM’s IMS, together with its dictionary, requires the user to learn two distinct data models).

Another consequence is that authorized users can easily extend the catalog to become a full-fledged, active relational data dictionary whenever the vendor fails to do so.

Comprehensive data sublanguage rule.

Rule 5: A relational system may support several languages and various modes of terminal use (for example, the fill-in-the-blanks mode). However, there must be at least one language whose statements are expressible, per some well-defined syntax, as character strings and that is comprehensive in supporting all of the following items:

  • Data definition.
  • View definition.
  • Data manipulation (interactive and by program).
  • Integrity constraints.
  • Authorization.
  • Transaction boundaries (begin, commit and rollback).

The relational approach is intentionally highly dynamic — that is, it should rarely be necessary to bring the data base activity to a halt (in contrast to nonrelational DBMS). Therefore, it does not make sense to separate the services listed above into distinct languages.

In the mid-’70s, the Ansi Standards Planning and Requirements Committee generated a document advocating 42 distinct interfaces and (potentially) 42 distinct languages for DBMS. Fortunately, that idea has apparently been abandoned.

View updating rule.

Rule 6: All views that are theoretically updatable are also updatable by the system.

Note that a view is theoretically updatable if there exists a time-independent algorithm for unambiguously determining a single series of changes to the base relations that will have as their effect precisely the requested changes in the view. In this regard, “update” is intended to include insertion and deletion as well as modification.

High-level insert, update and delete.

Rule 7: The capability of handling a base relation or a derived relation as a single operand applies not only to the retrieval of data but also to the insertion, update and deletion of data.

This requirement gives the system much more scope in optimizing the efficiency of its execution-time actions. It allows the system to determine which access paths to exploit to obtain the most efficient code. It can also be extremely important in obtaining efficient handling of transactions across a distributed data base. In this case, users would prefer that communications costs are saved by avoiding the necessity of transmitting a separate request for each record obtained from remote sites.

Physical data independence.

Rule 8: Application programs and terminal activities remain logically unimpared whenever any changes are made in either storage representations or access methods.

To handle this, the DBMS must support a clear, sharp boundary between the logical and semantic aspects on the one hand and the physical and performance aspects of the base tables on the other; application programs must deal with the logical aspects only.

Nonrelational DBMS rarely provide complete support for this rule — in fact, I know of none that do.

Logical data independence.

Rule 9: Application programs and terminal activites remain logically unimpared when information-preserving changes of any kind that theoretically permit unimpairment are made to the base tables.

Take the following two examples: splitting a table into two tables, either by rows using row content or by columns using column names, if primary keys are preserved in each result; or combining two tables into one by means of a nonloss join (Stanford University and MIT authors call these joins “lossless”). To provide this service whenever possible, the DBMS must be capable of handling inserts, updates and deletes on all views that are theoretically updatable. This rule permits logical data base design to be changed dynamically if, for example, such a change would improve performance.

The physical and logical data independence rules permit data base designers for relational DBMS to make mistakes in their designs without the heavy penalties levied by nonrelational DBMS. This, in turn, means that it is much easier to get started with a relational DBMS because not nearly as much performance-oriented planning is needed prior to “blast-off.”

Integrity independence.

Rule 10: Integrity constraints specific to a particular relational data base must be definable in the relational data sublanguage and storable in the catalog, not in the application programs.

In addition to the two integrity rules (entity integrity and referential integrity) that apply to every relational data base, there is a clear need to be able to specify additional integrity constraints reflecting either business policies or government regulations.

Assume the relational model is faithfully reflected. Then, the additional integrity constraints are defined in terms of the high-level data sublanguage and the definitions stored in the catalog, not in the application programs. Information about inadequately identified objects is never recorded in a relational data base. To be more specific, the following two integrity rules apply to every relational data base:

  • Entity integrity. No component of a primary key is allowed to have a null value.
  • Referential integrity. For each distinct nonnull foreign key value in a relational data base, there must exist a matching primary key value from the same domain.

If, as sometimes happens, either business policies or government regulations change, it will probably become necessary to change the integrity constraints. Normally, this can be accomplished in a fully relational DBMS by changing one or more of the integrity statements that are stored in the catalog.

In many cases, neither the application programs nor the terminal activities are logically impaired.

Nonrelational DBMS rarely support this rule as part of the DBMS engine, where it belongs. Instead, they depend on a dictionary package, which may or may not be present and can readily be bypassed.

Distribution independence.

Rule 11: A relational DBMS has distribution independence.

By distribution independence, I mean that the DBMS has a data sublanguage that enables application programs and terminal activities to remain logically unimpaired:

  • when data distribution is first introduced (if the originally installed DBMS manages nondistributed data only);
  • when data is redistributed (if the DBMS manages distributed data).

Note that the definition is carefully worded so that both distributed and nondistributed DBMS can fully support Rule 11. IBM’s SQL/DS and DB2, Oracle Corp.’s Oracle and Relational Technology, Inc.’s Ingres (all nondistributed in present releases) fully support this rule.

This has been demonstrated as follows: SQL programs have been written to operate on nondistributed data (using System R) run correctly on distributed versions of that data (using System R*, the IBM San Jose Research Laboratory prototype), and the distributed Ingres project at the University of California at Berkeley has shown the same capability for the Quel language of Ingres.

It is important to distinguish distributed processing from distributed data. In the former case, work (for example, programs) is transmitted to the data; in the latter case, data is transmitted to the work. Many nonrelational DBMS support distributed processing but not distributed data. The only systems that support the concept of making all the distributed data appear to be local are relational DBMS — these are prototypes right now.

In the case of a distributed relational DBMS, a single transaction may straddle several remote sites. Such straddling is managed entirely under the covers — the system may have to execute recovery at multiple sites. Each program or terminal activity treats the totality of data as if it were all local to the site where the application program or terminal activity is being executed.

A fully relational DBMS that does not support distributed data bases has the capability of being extended to provide that support while leaving application programs and terminal activities logically unimpaired, both at the time of initial distribution and whenever later redistribution is made.

There are four important reasons why relational DBMS enjoy this advantage:

  • Decomposition flexibility in deciding how to deploy the data.
  • Recomposition power of the relational operators when combining the results of subtransactions executed at different sites.
  • Economy of transmission resulting from the fact that there need not be a request message sent for each record to be retrieved from any remote site.
  • Analyzability of intent (owing to the very high level of relational languages) for vastly improved optimization of execution.

Nonsubversion rule.

Rule 12: If a relational system has a low-level (single-record-at-a-time) language, that low level cannot be used to subvert or bypass the integrity rules and constraints expressed in the higher level relational language (multiple-records-at-a-time).

In the relational approach, preservation of integrity is made independent of logical data structure to achieve integrity independence. This rule is extremely difficult for a “born-again” system to obey because such a system already supports an interface below the relational constraint interface. Vendors of “born-again” systems do not appear to have given this problem adequate attention.

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Industry news…..

Railsimulator.Com, the simulation games specialist which trades as Dovetail Games, plans to recruit over 50 people across all areas of games development. The Kent-based company is expanding after securing a contract with Microsoft to re-release its Flight Simulator X title and also build new titles based on the same technology.

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IT/ computing placement – 12 months at Derbyshire County Council

Vacancy details – closing date January 16th 2015 – don’t forget Careers + Employment Services can help with your application. 🙂

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Looking for a placement?

Opportunities for Tech and Cyber Security Internships – get in touch now 🙂

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