Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 23–26___________________________________________________________________________________________________________Management of digital records with RADAR by data dilution into Complex System
Anne Jeannin-Girardon

ICube laboratory, University of Strasbourg, UMR CNRS 7357, Strasbourg, France,
anne.jeannin@unistra.fr
Alexandre Bruyant

ICube laboratory, University of Strasbourg, UMR CNRS 7357, Strasbourg, France,alexandre.bruyant@etu.unistra.fr
Nicolas ToussaintComplex System Digital Campus UNESCO Unitwin, Manchester Metropolitan University, UK,n.toussaint@mmu.ac.uk
Ismaila Diouf

Complex System Digital Campus UNESCO Unitwin, Cheikh Anta Diop University, Dakar – Senegal,
isma.diouf@gmail.com
Pierre Collet

ICube laboratory, University of Strasbourg, UMR CNRS 7357, Strasbourg, France, pierre.collet@unistra.fr
Pierre Parrend

ICube laboratory, University of Strasbourg, UMR CNRS 7357, ECAM Strasbourg-Europe, France,pierre.parrend@unistra.fr

Abstract
Storing sensitive data in a centralized way can lead to significant loss, should the central node or networks links be
defective as is the case in numerous countries, especially developing countries. Consequently, this paper proposes
to deal with this problem by diluting sensitive data into an ecosystem of machines thanks to a platform called
RADAR (Robust Anonymous DAta Records) that automatically fragments, encrypt, replicates and distributes data
all over the network for anonymized, robust, encrypted storage and backup. RADAR gets nodes of a network tocooperate for storage and backups of sensitive data, even in case of non-reliable IT infrastructure.
Keywords: Epidemic replication, distributed storage, complex systems, data dilution, anonymity

1.

problems inherent to an unreliable network while at thesame time, providing a reliable enough Quality OfService (QOS) that may satisfy the demands of hightech countries.

Introduction

In some of the most important countries around theworld such as India, China but also in Africa, Internet
access is still precarious and therefore not reliable. Thismeans that it is difficult for such nations to setup anduse digital platforms that would help them to bridge thegap with highly connected countries which, on theirside, are plagued with the opposite problem: having avery reliable network infrastructure makes them want todevelop deterministic and perfect data management
systems that can simply not be implemented in practice.
The RADAR system presented in this paper proposes amore pragmatic approach, that can overcome the2.

Related work

Distributed databases all use data replication to improverobustness.
Most approaches focus on improving access time,
bandwidth consumption while guaranteeing thatabsolutely no piece of data can be lost. This is very
costly and very difficult to achieve. Paper [1] proposes ahierarchical approach to ensure replicas are consistentover the network. The cluster of databases used toCopyright © 2018, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 23–26___________________________________________________________________________________________________________implement this approach stores newest data at thetopmost level, while the lowest levels propagate replicas
from one level to the next in an epidemic way. This
allows them to define a range of QOS that is related tothe consistency level that is required by the clientapplication. Epidemic replication protocol is widelyused to replicate data consistently in distributed
databases. This approach is evaluated in [2]: in practice,
it is very difficult to propose an analytic methodology toevaluate the variables associated with epidemic
replication (such as bandwidth consumption, replicas
consistency, ...) so the authors propose a simulator
allowing one to optimize the parameters of theirreplication algorithms that scale with their network
configuration. Across large scale networks, such as datagrids, hybrid approaches are used, such as in [3]. In
order to ensure efficient access to the distributed data of
the grid, a scalable replica management system (usingboth hierarchical and flat topologies) and a dynamic
data distribution algorithm are used jointly. The
hierarchical topology contains nodes propagating
replicas to direct ancestors/children while the flat
topology propagates replicas using a Peer-to-Peerprotocol. Such protocols are also widely used in
distributed databases systems. Data in Peer-to-Peersystems are often assumed to be static and not subject tomany updates. But, for many applications, data arefrequently added, deleted or updated. In paper [4], theauthors propose an epidemic replication method
dedicated to Peer-to-Peer systems. One main
characteristic of Peer-to-Peer systems is that noassumptions can be made on whether the machines of
the network are online or not. Using rumor spreading
algorithms [5] and push/pull methods so that peers canrequest updates or send updates to peers that sent arequest update, the authors ensure some level ofconsistency for the data and achieve low latencies.

complicated hierarchical architectures or algorithms inorder to ensure data consistency. No assumption are
made about the level of reliability of the network on
which RADAR is deployed, allowing unreliableinfrastructures to use RADAR for distributed datastorage. Moreover, the model ensures security and canthus be used for sensitive data storage such as medicalrecords.
3.

Proposed RADAR model

Complex Systems
A Complex System can be defined as a set of
autonomous entities in interaction, exhibiting multilevel emergent behaviour where (as Aristotle said some
2300 years ago) the whole is more than the sum of theparts. The typical example is turbulence. The local
behaviour of particles results at a higher scale in theapparition of swirls and vortices that can be describedusing Navier-Stokes equations. However theseequations do not appear at all at the particle level.
RADAR implements storage entities that have no global
picture of the network, but that as a whole, behave like areliable and secure data storage system: machines relyon each other to automatically backup their data.
Modus operandi
The whole RADAR system is composed of many(ideally more than 10) loosely interconnected machines.
Because the network is deemed unreliable, machines
never “expect” anything from other machines unlessthey need to recover from a complete data loss.
Therefore, under normal operation mode, machines:● backup their own data by sending it to n othermachines they know about in the RADAR
network (using a non-connected protocol such
as UDP, because connected mode could
request a too high QOS from the availableinternet network),● accept encrypted pieces of data from othermachines of the RADAR network for backup.
Whenever a machine needs to store a new local piece ofdata, it is (depending on the required level of security ofthe data) possibly chopped into several pieces that aretimestamped, encrypted using the local cryptographic
key of the machine, replicated and depending on therequired level of reliability, sent to n other machines
randomly chosen in the network (the more required

The work presented in this paper proposes to protect
and increase the robustness and security of sensitive
data by chopping it into several pieces, encrypting thepieces, replicating the pieces and sending the replicas
randomly among a group of machines belonging to theRADAR network, resulting in a “dilution” of theoriginal data in a Complex System (a number ofautonomous machines interacting together to create anetwork architecture, whose emergent properties arerobustness and security). The RADAR model relies on
Complex Systems principles and does not need to define24

Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 23–26___________________________________________________________________________________________________________reliability, the greater n) for backup, thus preserving theanonymity of the data owner since full information
cannot be retrieved from a fragment. Fragmentation ofsensitive data can either be based on semantics, orsimply size. For instance, in our current application,
medical records are fragmented into:
PPD: patient personal data (name, address, telephone
number, e-mail address, ...),
MTD: medical textual data (sex, date of birth, weight,
blood pressure, a.s.o. along with symptoms,
diagnostics, medication, ...),
DMD: digital medical data (medical imaging,audiogram, electrocardiogram, ...),
PLD: private life data not related to medical condition(death of brother, ...) for a personal follow-up of
the patient.
In order to further ensure data anonymity, for eachpatient, each fragment is encrypted and sent separatelyto n machines chosen at random among the knownmachines. This means that if data is stolen on a given
machine, patient data is not compromised because for aparticular patient, each machine will keep only one of
the 4 (encrypted) fragments. For instance, if the MTD
fragment is stolen, the attacker has no information about
the personal data of the patient such as his/her name.

to enough machines in the network to hope to gain
access to all four fragments belonging to a particularpatient, which in practice is not feasible if the network
is made of hundreds of machines situated in different
places (hospitals, medical centers spread across thecountry).
Hybrid encryption
RADAR uses the RSA [6] asymmetric encryption
algorithm with 2048 bits keys: it is an asymmetricalgorithm that uses a public key to encrypt the data, anda private key for decryption. Unfortunately, thisalgorithm is computationally intensive and cannot
natively encrypt data that are longer than its key (2KB
in our case). In order to circumvent these limitations, we
use RSA in conjunction with AES [7], a much faster but
symmetric algorithm (the same key must be used forencryption and decryption).

Security

Fig. 1. Hybrid encryption used by RADAR●The data is encrypted via AES by a randomly generated
key, that is itself encrypted with RSA (cf. Fig. 1).
This makes it possible to verify the origin and theintegrity of the data. A hash code is generated using thedata to be signed. The code is encrypted thanks to theprivate key of the sender. The receiver can verify theauthenticity of the signature by deciphering the hashcode and by comparing it with a new code generated onthe data.
Each node receives an RFC 5280 signed certificatedelivered by the administrator of the network, thatguarantees that the node is indeed part of the network.

If a hacker gets hold of the cryptographic key
of one machine and gets access to thismachine, only the local data is compromised.
All other data stored as a backup for othermachines remain protected because it isencrypted with the cryptographic keys of theother machines.
● If a hacker gets hold of all the cryptographic
keys of all the machines in the network, andphysically gets access to one machine, the dataof the machine is compromised (as above) andthe hacker will be able to read all the fragments
stored by the machine as a backup for othermachines. However, because the different
fragments have been randomly sent to different
machines, it is highly unlikely that all 4 PPT,MTD, DMD and PLD fragments for one
patient will be found in the backup files holdby one machine.
To sum up, with RADAR, the only way to have accessto a particular patient’s record that is not locally stored
on one machine is to have full access (with passwords)

Data loss and recovery
RADAR comes in when a local database is completely
lost and not recoverable. What happens next is that theperson in charge with the database that has disappearedcontacts the administrator of the network, asking for a
database rebuild. When the new machine is ready, it isadded to the network again, but in recovery mode. The
rebuild starts with one machine of the network sending25

Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 23–26___________________________________________________________________________________________________________to the other machines it knows (epidemic messagetransmission) the message that all files that have been
received in the past from the failed machine must be
sent back to it. The machine in recovery mode thenstarts to receive from the whole network all the data it
previously sent. It uses the timestamps of the data toreconstruct its database in a consistent way. During therecovery, the machine keeps an account on the numberof replicas it has received for all its data. After recovery,
the machine sends again the missing number of replicas
of a given data to random machines, so that it sums backto n.

4.

Conclusion

Where most other systems focus on perfect consistency
and reliability resulting in over-complex time
consuming algorithms which, eventually, are not 100%bullet-proof, RADAR accepts that data may be lost, but
focusses on reducing such occurrence to very low
probability. It offers a system that is simple, whilerobust enough to work over an unreliable network andensures data is stored anonymously when dealing withsensitive data such as medical records. RADAR iscurrently tested on a huge patient cohort of more than
300 000 patients over 60 nodes in India. Following
work will concentrate on reliability thanks to large scaletesting using network simulation tools.

Data loss probability
It is very difficult to estimate the probability that when amachine has lost all its data, all n copies of a given
patient record are also lost at the same time. Computing
this probability is so complex that most similar data
replication systems use network simulation tools such as
Chive [2] and Network Simulator (NS) [8] to run
empirical tests to evaluate the robustness of theirapproach. Using the same methods shows that for most
realistic RADAR configuration and failure probabilities,
it is very difficult to effectively lose data.

Acknowledgements
The authors thank the N-Light endowment fund thatfunds this project, as well as the JIVA Ayurveda Indiancompany, with which RADAR is currently tested.

References
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Depending on n, the number of replications that ischosen, for the reliability of a particular network,
expected disk consumption is in average n+1 times thedisk consumption of a single machine. Indeed, if onemachine sends n copies of its data to other machines in
the network, this means that it will receive data from (inaverage) n other machines in the network. Scaled to themedical information system of Senegal, (for which
RADAR was initially designed) the system could use
around 1000 medical centers (with at least one machine
per center) to deal with a population of 15M inhabitants.
With a uniform distribution of patients per center(which is of course not the case), each machine would
store the medical records of 15k local patients. Withn=3, this means that each machine would need to store
the equivalent of 45k medical records, which is areasonable amount by 2018 disk storage space. This is
to be compared with the cost needed to create, maintainand operate a data center capable of dealing with all 15
million medical records of the country in a reliable andsecure way over an unreliable Internet network.

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