Constructive Privacy for Shared Genetic Data

Fatima-zahra Boujdad and Mario Sudholt

IMT Atlantique, 4 rue Alfred Kastler, Nantes, France

Keywords:

Genetic Data, Privacy, Outsourcing, Watermarking, Fragmentation, Encryption.

Abstract:

The need for the sharing of genetic data, for instance, in genome-wide association studies is incessantly gro-

wing. In parallel, serious privacy concerns rise from a multi-party access to genetic information. Several

techniques, such as encryption, have been proposed as solutions for the privacy-preserving sharing of genomes.

However, existing programming means do not support guarantees for privacy properties and the performance

optimization of genetic applications involving shared data. We propose two contributions in this context.

First, we present new cloud-based architectures for cloud-based genetic applications that are motivated by the

needs of geneticians. Second, we propose a model and implementation for the composition of watermarking

with encryption, fragmentation, and client-side computations for the secure and privacy-preserving sharing of

genetic data in the cloud.

1 INTRODUCTION

Information about the human genome has become

highly valuable for development of new treatments

of genetic-based diseases. Advanced sequencing

technologies (NGS) (Behjati S, 2013) have made it

much easier to obtain complete genetic data of human

beings. However, in general genetic samples are not

sufﬁciently available to genetic research. Indeed, ge-

netic research has often be conducted collaboratively

between several (groups of) geneticians. Doing so,

more meaningful sizes of genetic cohorts can be es-

tablished and allow for accurate results. For instance,

Genome Wide Association Studies (GWAS) use two,

if possible, large sets of genetic data (e.g., in the form

of positions from genomes (Cousin et al., 2006)), case

data which belongs to subjects of the studied dise-

ase and control data that is obtained from healthy do-

nors. Control data is particularly difﬁcult to obtain

because it has to be provided on a voluntary basis by

healthy individuals. This is one case in which resear-

chers want to share and work on the already available

corresponding data.

Though sharing in this fashion seems to be straig-

htforward, the very private aspect of genetic informa-

tion (Erlich and Narayanan, 2014) pushes genetici-

ans to protect their collected data and hence, ham-

per the practical and ﬂexible sharing of genetic data.

Instead of sharing raw data, genetic research often re-

lied on public aggregated data, e.g., allele frequency,

in a false belief that this procedure was privacy-

preserving. However, since the attack by Homer and

et al. (Homer et al., 2008), the aggregated informa-

tion has been retired from public access

. In fact,

the attack shows how to infer a speciﬁc individual

presence in a study based on those aggregated data

only. This clearly threatens the privacy of patients.

As a consequence, sharing of genetic data, notably via

cloud-based services is very limited currently and per-

formed using very restrictive queries on genetic data-

bases.

Secure and privacy-preserving sharing of high vo-

lumes of genetic data constitute a very active rese-

arch ﬁeld nowadays. Data is shared using a client/-

server architecture where the server is often a cloud

provider storing and processing, e.g., homomorphi-

cally encrypted data (Lu et al., 2015; Zhang et al.,

2015), which enables computations to be directly per-

formed on encrypted data; another architecture con-

sists in the collaboration between different biomedical

sites which make use of multi-party computation pro-

tocols. However, the corresponding approaches, e.g.,

(Liina Kamm et al., 2013; Tang et al., 2016), only

handle a limited number of sites in the scenario and

are yet to be extended so to handle realistic scenarios

for a wider sharing of genetic data.

Some approaches have proposed the combi-

nation of different privacy-enhancing techniques,

e.g., homomorphic encryption, multi-party compu-

http://help.gwascentral.org/data/download/

Boujdad, F. and Sudholt, M.

Constructive Privacy for Shared Genetic Data.

DOI: 10.5220/0006765804890496

In Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018), pages 489-496

ISBN: 978-989-758-295-0

489

tation protocols to provide secure sharing of gene-

tic data (McLaren et al., 2016). The combination

of data fragmentation and client-side computations

has been proposed in the context of an outsourcing

schema (Wang et al., 2009). It locally stores identi-

fying genome components, i.e., SNPs, while compu-

tations on the common and publicly known parts of

the human genome are outsourced. Combining dif-

ferent techniques for security purposes has also been

explored for other related purposes. For instance, in

(Ciriani et al., 2010), a combination of encryption and

fragmentation is used to protect outsourced databases.

For multimedia data, (Bouslimi et al., 2016) suggest a

combination of encryption and watermarking to trans-

mit images through an untrusted network securely.

A crucial result of the current situation is that the

secure and privacy-related handling of shared data re-

quires different technologies to be composed in or-

der to handle realistic collaboration scenarios. Howe-

ver, no such general compositional approach for the

sharing of genetic data exists. In particular, while the

existing approaches focus on conﬁdentiality proper-

ties, ownership and integrity properties have received

few attention.

We build on ideas from the PRIVGEN project

, in

which researchers in genetics and computer science

study new approaches for distributed analyses over

shared genetic data. In this paper, we provide a com-

positional approach supporting ownership and inte-

grity properties by extending the approach by (Cher-

rueau et al., 2015) to genetic data scenarios. Cherru-

eau et al. allow the composition of encryption, data

fragmentation and localized computations to establish

conﬁdentiality of sensitive data. Concretely, we pre-

sent two main contributions:

• We present and discuss several new architectures

and scenarios for the cloud-based sharing of ge-

netic data.

• We add watermarking techniques to the approach

by (Cherrueau et al., 2015) in order to support ow-

nership and integrity properties of shared genetic

data. Concretely, we present a language-based ap-

proach for building applications and servers for

the composition of privacy-preserving applicati-

ons manipulating shared genetic data.

We also

present an algebraic theory that allows for the op-

timization of such applications and servers.

The paper is structured as follows. Sec. 2 presents

basic information about the sharing of genetic data

Privacy-preserving sharing and processing of genetic

data, http://www.privgen.cominlabs.u-bretagneloire.fr

The Idris implementation of our approach is

available at: https://github.com/BoujdadFz/PrivGen-

Rep/blob/master/ coshed.idr.

? data

Trusted

party

case data

Cloud

(storage

&nonsensitive

computations)

sensitive computations

control data

Data

. . .

Figure 1: Genetic data sharing via a trusted party.

and introduces new corresponding software architec-

tures for this purpose. In Sec. 3 we present our ap-

proach COSHED for the compositional construction

of systems for the sharing of genetic data. We close

with a conclusion.

2 GENETIC DATA SHARING

Together with our partners of the PRIVGEN project,

we are studying more ﬂexible architectures that sup-

port wider sharing of genetic cohorts in the Cloud

while preserving data privacy properties. Existing ge-

netic data and computation servers are limited essen-

tially to simple client-server systems that allow gene-

ticians to perform highly restricted stateless queries.

In contrast, the partners of PRIVGEN are working on

more powerful collaborative data-sharing architectu-

res. Concretely, the geneticians in the PRIVGEN pro-

ject are interested in a scalable architecture that al-

lows for sharing between multiple owners of genetic

data (research or private institutions) and researchers

in genetics.

2.1 Architectures

Based on the requirements of geneticians we are pro-

posing new architectures that allow for genetic data

sets provided by different organizations to be shared.

Sharing is performed using trusted parties mediating

data that is stored and (partially) manipulated in fede-

rated clouds. In the following, we propose two new

such architectures. These architectures differ, in par-

ticular, in the policies for genetic data sharing (GDS)

they allow for. Additionally, we motivate that water-

marking techniques are a crucial means to satisfy ow-

nership and integrity properties in this context.

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

490

case data

control data

? data

...

sensitive computations

cloud

(storage

&nonsensitive

computations

local

server

Figure 2: Genetic data sharing via local servers and cloud.

2.1.1 Trusted Party Architecture

The ﬁrst architecture we propose, shown in Fig. 1,

enables data to be shared among different geneticians

(G in the ﬁgure) and to be transferred to a cloud infra-

structure. Communications are mediated by a trusted

party (TP) that can enforce privacy and security pro-

perties of the data and computations shared among

the other participants. Because of the strong control

provided by the mediating trusted party, this architec-

ture is particularly suitable to cooperation scenarios

between partners having different GDS policies.

Since this kind of architecture includes data to

be transferred from the geneticians (or corresponding

owner institutions) to both the Cloud and the TP, ow-

nership and integrity properties of shared genetic data

are crucial in addition to more frequently-used conﬁ-

dentiality properties.

Trusted parties can be integrated into genetic ap-

plications in different ways: Kantarcioglu et al. (Kan-

tarcioglu et al., 2008) employ a trusted entity for sto-

ring encrypted genetic data and processing (anony-

mous) computations on it; Xie et al. (Xie et al., 2014)

uses a trusted component for key distribution and ma-

nagement. We rather allow for direct access to gene-

tic data by the trusted party in our architecture, which

is in fact conform to real-world applications (Gulcher

et al., 2000).

This architecture can be generalized to allow for

sharing as part of federated Clouds. In Sec. 3.1.1 we

harness such an architecture in the context of a con-

crete sharing scenario.

2.1.2 Local Computations Architecture

Fig. 2 shows a model for computations performed

by geneticians that all have their own infrastructure

able to handle computations on genetic datasets of

bearable sizes. In this case, sharing of data is done

through the Cloud. A typical workﬂow may deﬁne a

dataset, encrypt it for transfer in the Cloud where it

may undergo some privacy-neutral processing, before

ﬁnally being transmitted to its ﬁnal recipient. This

workﬂow requires fragmentation techniques, crypto-

graphic techniques and client-side computations. In

this architecture as well ownership properties have to

be satisﬁed which can be assured by watermarking.

This architecture and corresponding workﬂows re-

quire closer integration between the collaborators, no-

tably their GDS policies.

3 PRIVACY FOR SHARED

GENETIC DATA

The architectures manipulating shared genetic data

introduced in the previous section may easily lead to

violations, for instance, of privacy properties. Such

violations may stem from simple programming errors

or more difﬁcult errors in the applications’ logic. Our

approach is based on the C2QL approach by (Cherru-

eau et al., 2015) who provide composition and query

languages for the secure and privacy-preserving pro-

gramming of distributed applications. In the follo-

wing, we ﬁrst review the basic mechanisms of their

approach before detailing ours.

C2QL enables the development of privacy-aware

applications by the implementation of distributed al-

gorithms composing computations involving encryp-

ted and fragmented data, as well as client-side compu-

tations. SQL-like queries are supported on top of pos-

sibly encrypted and fragmented data. The approach

essentially consists of two parts: language support for

the development of secure and privacy-preserving ap-

plications and an algebraic theory supporting corre-

sponding optimizations and correctness proofs.

Language Support. The C2QL language provides

de/constructors for the encryption and the fragmen-

tation of data, respectively denoted Decrypt, Crypt,

Defrag and Frag. Client-side computations are ini-

tiated automatically depending on the fragmentation

and encryption status of data. The constructors are

used as part of a domain speciﬁc language that is em-

bedded in the Idris (www.idris-lang.org) program-

ming language. Idris programs are used to compose

secure and privacy-aware applications as well as the

queries over genomic data. Idris’ dependent type sy-

stem enables the veriﬁcation of basic secure proper-

ties at compile time, e.g., that a given encrypted data

fragment can only be used after decryption.

Constructive Privacy for Shared Genetic Data

491

1 -- Bas ic va lue ty pe s

2 da ta Ty = BOO L | N AT | TE XT

3 | C RYPT Crypt Ty Ty

5 -- At tr ibute : ( c ol umn name , va lu e t ype)

7 Attri bu te : Ty pe

8 Attri bu te = ( S tring , Ty )

10 -- Tab le sc he ma : l ist of at tr ib ut es

12 Sch em a : T ype

13 Sch em a = L ist A tt ri bu te

15 -- D B e nv ir on me nt : v ec tor of ta ble s che ma s

17 En v : Na t → T yp e

18 En v n = V ect (S n ) S che ma

Figure 3: Basic data types.

Figure 3 shows the basic types implementing data

bases. Figure 4 shows the abstract deﬁnitions of

1 -- AD T ( a lgebr ai c dat a t yp e) f or pr iva cy o pe ra to rs

2 da ta Pr ivy : ( e nv0 : E nv n ) → ( en v1 : En v m ) →

3 (∆ : S ch ema ) → T ype wh er e

5 -- en cry pt s an at tr ib ut e i n an en vi ro nm en t

6 C r yp t : ( a : At tr ib ute ) → ( c : Cr yp tTy ) →

7 { au to p : EnvEl em a env} →

8 P ri vy en v ( c ryptE nv c a en v ) [ ]

10 -- fr ag ments an en vir . at the ’m ost rig ht ’ s ch em a

11 -- @ p pro of tha t ‘δ‘ ⊆ ‘( la st env ) ‘

12 Fr ag : (δ: Sc hem a ) → { au to p : I nc δ ( la s t en v )} →

13 P ri vy en v ( f rag En v δ en v) []

15 -- AD T f o r da ta re co ve ry op er at or s

16 da ta Qu ery : (∆ : Sch em a ) → Ty pe wh ere

18 -- dec ry pt s val ue s of ‘a ‘ u sing in fo rm at io n ‘ d‘

19 -- @ p1 proo f t ha t v al ues of ‘a ‘ ar e e nc ry pt ed

20 -- wi th sc hem a ‘c ‘.

21 De cr yp t :

22 (a : Att ri bu te ) → ( d : De cr yp t c ) →

23 { de fa ul t Re fl p1 : ( CR YP T c t ) = ( sn d a )} →

24 { au to p2 : Ele m a ∆} →

25 Q ue ry ∆ → Quer y ( r ep la ce On a ( fs t a , t ) ∆)

27 -- defrag me nt s va lu es of ‘q1 ‘ an d ‘ q2 ‘

28 De fr ag : ( q1 : Que ry δ) → ( q2 : Que ry δ’) →

29 Q ue ry ( d el et e Id (n ub (δ + + δ ’ ) ) )

Figure 4: Privacy-enforcing operators and queries.

privacy-enforcing operators (type Privy) and the en-

cryption/fragmentation constructors as well as queries

(Query) that are constructed by, if necessary, decryp-

ting and defragmenting shared data.

Algebraic Theory. The compositions of privacy-

enforcing operators and query operations are linked

by numerous algebraic laws that express, for instance,

the commutativity of certain compositions of operati-

ons. Based on these laws, applications over shared

data can be transformed. Such transformations are

useful, for instance, in order to distribute data by

fragmentation (and thus helping privacy through de-

identiﬁcation of data) or optimize application per-

formance (in particular, by harnessing the cloud to

handle most of the benign computations).

Henceforth, we denote a set of attributes (co-

lumns) in a given relational database as a and use ◦

as the symbol for composition. SQL-like projection

is denoted by π

while selection is σ

where p is the

selection predicate. The algebraic operators used are:

• crypt

(s,a)

, decrypt

(s,a)

: encryption and decryption

operators parameterized by a schema s (e.g., AES)

and the target attribute a to be encrypted in the

relational database.

• frag

, defrag

: column-oriented fragmentation

and defragmentation operators

Figure 5 shows some examples of the algebraic laws

that are used later.

a ¯a

◦ defrag

≡ defrag

◦ (π

,π

¯a

) (1)

pa∧p ¯a

◦ defrag

≡ defrag

◦ (σ

,σ

p ¯a

) (2)

◦ decrypt

(s,a)

≡ decrypt

(s,a)

◦ π

(3)

if dom(p) /∈℘(a)

◦ decrypt

(s,a)

≡ decrypt

(s,a)

◦ σ

(4)

Figure 5: Commutation laws.

3.1 The COSHED Approach

Our approach for a COnstructive SHaring of gEnetic

Data (COSHED) extends the C2QL approach with

watermarking functions. In fact, genetic data water-

marking is a promising technique for integrity, tra-

ceability or ownership protection. Concretely, we

have added the watermarking scheme in (Iftikhar

et al., 2015) that supports ownership and integrity pro-

tection properties for digital genetic data. In the follo-

wing, we present operators for watermarking and the

detection of watermarks that can be composed with

the ones for encryption and fragmentation. We also

introduce the corresponding algebraic laws that go-

vern the relationship between the different privacy-

enforcing techniques.

Language Support. Watermarks are represented as

a type WATERMARK whose ﬁrst argument represents the

watermarking scheme (for now we have implemented

only one scheme GIG (Iftikhar et al., 2015)). We have

implemented operators wat

and detectw

(a,secrets)

see Fig. 6, respectively for watermark application (as

part of the privacy-enhancing technologies of ADT

Privy) and watermark detection as used in queries

(ADT Query). The wat

operator has as parame-

ters the attribute that indicates which columns have

to be watermarked in addition to two implicit argu-

ments for compile-time veriﬁcation. More precisely,

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

492

1 -- @G IG st an ds for Ge nI nf oG ua rd wat er ma rki ng sc he me

2 da ta WmT y = G IG

3 da ta Ty = .. . | W AT ER MA RK Wm Ty Ty

5 -- a funct io n tha t wa te rm ar ks an at tr ib ut e

6 -- @ p i s a proof th at @ a is in ’env ’

7 wa tE nv : ( a: At tribute ) → ( wms : WmT y ) → ( en v : En v n)

8 → { au to p : So ( i sI nE nv a e nv )} → En v n

9 wa tE nv a wm s e nv =

10 map (\ s = > i f ( e le m a s) the n rep la ce On a

11 ( fst a, W AT ER MA RK wms (s nd a )) s

12 e lse s) e nv

14 -- AD T w ith i nf or ma ti on for w atermar k de te ct io n

15 da ta Rea dM : W mT y → T ype wh er e

16 RG IG : ( k :K ey ) → R ea dM G I G

18 -- wa te rmark ap plicati on op er at or

19 da ta Pri vy :

20 ...

21 Wat : ( a : A tt ri bute ) →

22 { au to p1 : So ( is Ra wT yp e ( sn d a )) } →

23 { au to p2 : So ( is In En v a e nv )} →

24 P ri vy en v ( watE nv a G IG en v ) [ ]

26 -- wa te rmark d et ec ti on o pe rator

27 da ta Que ry :

28 ...

29 dete ct w :

30 (a : Att ri bu te ) → ( i nf o : R ead M GI G ) →

31 { de fa ul t Re fl p1 : ( sn d a ) = ( W AT ER MA RK GIG t) }

32 → Q ue ry ∆ → { a uto p2 : El em a ∆} →

33 Q ue ry (( re pl ac eO n a ( fs t a , t ) ∆) ++[ M yT att oo ])

Figure 6: Watermarking operators.

p1 serves as a (pre-deﬁned) proof that the passed attri-

bute is not watermarked nor encrypted; p2 ensures its

membership in the targeted environment. Similarly,

in the query operator detectw

(a,secrets)

, p1 is a proof

that the parameter attribute was previously watermar-

ked with the right schema, i.e., GIG, so that water-

mark detection makes sense.

Laws. The watermarking schema GIG is reversible,

we can hence deﬁne the identity law

id ≡ detectw

◦ wat

(5)

Unlike for the encryption/decryption operators,

the watermarking schema is not a parameter because

the entire schema has already been deﬁned (Iftikhar

et al., 2015). Actually, most watermarking laws will

be speciﬁc to the watermarking schema, contrary to

encryption which essentially is a more general opera-

tion.

The second law stipulates that watermark de-

tection can be delayed to after decryption provided

that the watermark application took place before en-

cryption.

decrypt

(s,a)

◦ crypt

(s,a)

◦ detectw

◦ wat

≡

detectw

◦ decrypt

(s,a)

◦ crypt

(s,a)

◦ wat

(6)

Furthermore, watermark detection commutes with

projection:

◦ detectw

≡ detectw

◦ π

(7)

Provided that selection is not performed on wa-

termarked attributes, watermark detection commutes

with selection: if dom(p) ∩ a = Ø

detectw

◦ σ

= σ

◦ detectw

(8)

3.1.1 Cloud-based Association Studies

We are now ready to present our deﬁnition of the ad-

vanced architectures for sharing of genetic data. Con-

sidering a scenario for large genomic-wide associa-

tion studies (GWAS), a federated cloud architecture

as proposed in Sec. 2.1 is considered that satisﬁes the

characteristics required by geneticians from the PRI-

VGEN project:

• The public cloud provider should not be able to

get direct access to identifying data

• Geneticians/researchers should not be able to get

direct access to external identifying data, e.g., ge-

nomes.

• Ownership and integrity properties of the data

have to be satisﬁed.

Explanation of how these requirements are satis-

ﬁed in our architecture is given later in this section.

case data

Tru st ed

party

case data

Cloud

(storage

&nonsensitive

computations)

sensitive computations

control data

(Q1,Q2,Q3)

(Q2,Q3)

dem_datal

- dem_datar ;

- vcf files.

LeftCloud

RightCloud

Results

rightCloudTab1;

leftCloudTab

rightCloudTab2

Figure 7: Genetic data sharing scenario via a trusted party.

Scenario Architecture. We illustrate an implemen-

tation of a variant of the sharing through the trusted-

party architecture presented in Sec. 2.1, variant shown

in Fig. 7. This architecture variant is used as part of

a scenario for sharing genetic cohorts in GWAS. In

this scenario, genetic data is of two sorts: case data

represents the set of vcf ﬁles containing variants be-

longing to patients holding the studied disease, while

control data concerns healthy participants. As is often

the case in genetics, control data are not in sufﬁcient

Constructive Privacy for Shared Genetic Data

493

supply in research centers: data sharing with exter-

nal researchers that possess interesting control data is

therefore of high interest.

We present participants with their zip code, day of

birth (DoB), gender, information of whether the sub-

ject is contributing case or control data and their

corresponding vcf ﬁle. Had the database been local,

the representation would be in two tables as follows:

Subject (SubjectId,ZIP,DoB,Gender,CaseCtrl)

SubjectVcf (recordId,Variant,TypeVariant,

position,SubjectId)

For privacy-preserving outsourcing in the cloud

that ﬁts the aforementioned requirements deﬁned by

geneticians, four security techniques are applied in

the scenario:

• Fragmentation for conﬁdentiality: the triplet

(zip code,gender,DoB) forms a quasi-identiﬁer

(Sweenay, 2000): fragmentation is therefore used

to forbid any re-identiﬁcation attempts. There-

fore, we store the pair (zip code, gender) and

DoB in different fragments in different non-

communicating Clouds (denoted as LeftCloud

and RightCloud in Fig. 7);

• Encryption for conﬁdentiality: used for any data

that can not be fragmented nor it can be kept plain

at cloud level; in our scenario case, the vcf ﬁle

is symmetrically encrypted (ﬁeld-wise) except for

the position ﬁeld;

• Watermarking for ownership and integrity pro-

tection of genomes. In fact, genomes will be

accessed in clear format by the trusted party and

any unintended disclosure of data can also threa-

ten the ownership of data and its integrity.

• Client-side computations are used for TP com-

putations. This means that the geneticians need

to share (only) their decryption keys with the TP

which satisﬁes the ﬁrst requirement of genetici-

ans as any access to identifying data is performed

through the trusted party.

Querying a database is more efﬁcient when data is

plain. Therefore, increasing performance can be

achieved by decreasing the application of costly se-

curity techniques. One essential idea behind the com-

position of security and privacy-enhancing techniques

is foremost using any security method that will keep

data in plain format and does not threaten privacy. A

typical corresponding application case consists in in-

formation whose sensitivity results from it being asso-

ciated to other data. The triplet (zip code,gender,DoB)

is an example.

As part of the scenario, G1, G2 are genetic rese-

arch sites that initially each possess case data ﬁles;

G3 holds control data. We ﬁrst assume that every re-

searcher and genetic center already outsourced their

data to a cloud provider as introduced above. In the

scenario, G1 wishes to process an association study

over a disease X. For this purpose, G3’s control sets

are needed. The scenario then proceeds as follows:

1. G1 starts by requesting TP to perform an associa-

tion study over indicated variants (Q1,Q2,Q3).

2. TP asks G3 for authorization (A1) to use its cont-

rol data in the cloud for G1’s research.

3. Assuming G3 provided its authorization, TP can

apply distributed queries to the corresponding

cloud databases and thus get the necessary data

(demographic data and parts of vcf ﬁles).

4. After computations are done, TP communicates

the inferred results to G1 (in a secure manner e.g.,

an SSL connection).

The architecture can also accommodate special

cases of sharing. For instance, it may happen that

G1’s datasets are not sufﬁcient for some study: TP

then can search complementary data from other ge-

neticians which obviously requires their authorization

(A2).

To prepare for the implementation of the scena-

rio, we need to write correct queries, a process which

is performed using the laws about privacy-enhancing

compositions. We consider two queries, one for re-

trieving demographic data and another query to recu-

perate genetic data. The new tables of the new dis-

tributed database after applying the aforementioned

techniques become (cf. Fig. 7):

leftCloudTab (SubjectId,ZIP,Gender)

rightCloudTab1 (recordId,VariantWE,

TypeVarE,position,

SubjectId,)

rightCloudTab2 (SubjectId,DoB,CaseCtrl)

As the genetic application represented in the sce-

nario is an (abstract) genetic association computation

performed by the trusted party, both demographic and

genetic data should be provided by the cloud provi-

ders. The ﬁrst query over the distributed Subject ta-

ble is meant to return the zip code and the DoB of

male subjects holding the disease. In order to retrieve

this data from the distributed environment the query

should be split. Fig. 8a shows a suitable local version

of the query. In a distributed setting, we use com-

mutation laws to obtain a distributed query (Fig. 8b)

from a local one. As for the genetic data recovery

query, the goal is to retrieve some speciﬁc positions in

the genomes of the previously selected males demo-

graphic data (which are the results of the ﬁrst query,

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494

(zip,dob)

◦ σ

(gender=male∧caseCtrl=true)

(a) local query .

(zip,dob)

◦ σ

(gender=male∧caseCtrl=true)

◦

defrag

zip,gender

◦ frag

zip,gender

laws 1, 2 ↓

defrag

zip,gender

◦

(π

zip

◦ σ

(gender=male)

,π

dob

◦ σ

(caseCtrl=true)

)◦

frag

zip,gender

(b) distributed query .

Figure 8: Query for demographic case data recovery.

referred to as mdd in the ﬁgure). Therefore, the right

Variant and TypeVariant elements are returned. Simi-

larly, this second query is transformed from a local

formula (Fig. 9a) to a distributed one (Fig. 9b) using

the identity and commutation laws of encryption and

watermarking described earlier in Secs. 3 and 3.1, re-

spectively. The last distributed query obtained shows,

after law-driven transformations, that to have access

to both columns, a decryption step and a watermark

detection operation should be executed over the data,

a step that ’deconstructs’ the previous encryption and

watermarking introduction that were necessary for a

secure and privacy-preserving outsourcing process.

(variant,typeVar)

◦

((sub jectId∈mdd)∧(position=i,position= j,..))

(a) local query .

(variant,typeVar)

◦

((sub jectId∈mdd)∧(position=i,position= j,..))

◦

decrypt

variant,typeVar

◦ crypt

variant,typeVar

◦

detectw

variant

◦ wat

variant

laws 3, 4,6, 7,8 ↓

detectw

variant

◦ decrypt

variant,typeVar

◦

(variant,typeVar)

◦

((sub jectId∈mdd)∧(position=i,position= j,..))

◦

crypt

variant,typeVar

◦ wat

variant

(b) distributed query .

Figure 9: Query for genetic case data recovery .

1 sce na ri o : Ge ne ti cQ ue ry [ Sub je ctI d , ZIP , Gen der , DoB ,

2 Va ria nt , Ty peV ar , My Ta tt oo ]

3 sce na ri o = do

5 G1 ‘ S end Re qu est ‘ ( TP ,[ Q 1 ])

6 G1 ‘ S end Re qu est ‘ ( TP ,[ Q2 , Q2 ’] )

7 G1 ‘ S end Re qu est ‘ ( TP ,[ Q3 , Q3 ’] )

9 TP ‘ S end Re qu est ‘ ( L eft Cloud ,[ Q 1 ])

10 TP ‘ S end Re qu est ‘ ( R igh tC lou d , [Q2 , Q2 ’] )

11 TP ‘ S end Re qu est ‘ ( R igh tC lou d , [Q3 , Q3 ’] )

13 let q1 = Lef tC lo ud ‘ e xe cut eR eq ue st ‘ [ Q1 ];

14 let q2 = RightCl ou d ‘e xe cu te Req ue st ‘ [ Q2 , Q2 ’] ;

15 let q3 = RightCl ou d ‘e xe cu te Req ue st ‘ [ Q3 , Q3 ’] ;

17 demD at al ← L ef tC lo ud ‘ Se ndDa ta ‘ (TP , q1 )

18 demD at ar ← R ig ht Cl ou d ‘S end Dat a ‘ ( TP , q 2 )

19 vcfF il es ← R ig ht Cl ou d ‘S end Dat a ‘ ( TP , q 3 )

21 let r1 = dec ry pt Va ri an tW E ( A ESD " key 2 ") vcfFi le s ;

22 let r2 = dec ry pt Ty pe Va rE ( AES D " ke y1 ") r1 ;

23 let v cfF il es = d etect w Va riant W ( R GIG " wkey1 ") r 2;

24 let Da ta = defra g ( d ef rag d em Da tal d em Da ta r ) v cf Fi le s

26 TP ‘ R eturn Re su lts ‘ ( G1 , TP ‘C omp ute ‘ D ata )

Figure 10: Scenario implementation .

Scenario Implementation. The scenario imple-

mentation is given in Fig. 10: it initially sends query

requests from the genetician G1 to the trusted party TP

that forwards them to the two clouds where they are

executed. The resulting data is sent back to TP where

the genetic computation is performed. The correspon-

ding results are then communicated to G1.

1 -- En tit ie s

2 da ta En tity = Ge ne ti ci an | Tr us te dP | Cl ou d

4 G1 , TP , Lef tCl ou d , R ig ht Cl ou d : En tit y

5 G1 = G en et ic ia n ; TP = T ru st ed P

6 L ef tC lo ud = C lo ud ; Rig ht Cl ou d = Clo ud

8 l ef tClou dT ab , righ tC lo ud Tab1 , rig ht Cl ou dT ab 2 : Schem a

9 l ef tClou dT ab = i nd ex 1 Sa fe TP En v . ..

11 - - Qu er ie s

12 Q1 : Q ue ry [ S ub jec tId , ZIP , Gend er ]

13 Q1 = π [ Su bject Id , ZIP , G en der ]

14 $ σ ( Ge nd er == "m al e ") ( t oQ uer y le ft Cl ou dT ab );

16 - - A DT whi ch pa ra me te r in di ca te s t he expec te d re sul ts

17 - - o f an ex ch ang e or co mp ut at io n in vo lv in g gen et ic da ta

19 d ata G en et ic Qu er y : Sch em a → Ty pe whe re

21 S en dR eq ue st :

22 En ti ty → ( En ti ty , L ist ( Que ry ∆)) → G en et icQ ue ry ∆

23 Send Da ta : ...

24 Co mp ut e : .. .

Figure 11: Entities, queries and ADT for scenario building.

Fig. 11 shows the entity deﬁnitions for genetici-

ans, trusted parties and clouds, a query and the main

part of the ADT for genetic queries.

1 ... let r1 =

2 de te ct w Var ia nt WE ( AES D " ke y2 ") v cf Fi les -- e rr or

3 let r2 = de crypt T yp eV arE ( AE SD " k ey1 ") r1 ; . ..

Figure 12: Ill typed query.

Constructive Privacy for Shared Genetic Data

495

Ensuring Privacy Properties. Our approach, being

based on Idris, allows for proofs of certain safety pro-

perties (that entail security and privacy properties) to

be passed as arguments to operators that are part of

correctly built queries. For instance, if we try to wa-

termark a genetic data that is already encrypted, type

checking will not pass because of the proof p1 of the

wat operator that veriﬁes data has not been transfor-

med yet. Similarly, trying to detect a watermark from

data that has not been decrypted yet will give rise to a

type checking error, see Fig. 12.

4 CONCLUSION

In this paper we have pointed to the lack of program-

ming support for privacy-preserving applications that

manipulate shared genetic data. We have presented

two contributions: (i) new cloud-based architectu-

res for such applications that are motivated by con-

crete requirements from researchers in genetics and

(ii) a model and corresponding security- and privacy-

enhancing techniques for the development of such ap-

plications, notably using watermarking for the preser-

vation of ownership and integrity properties.

As future work, we are striving for the integra-

tion of other privacy-enhancing techniques, an efﬁ-

cient implementation of a general Java library for bi-

omedical analyses using shared genetic data, and its

application to real-world genetic analyses.

ACKNOWLEDGEMENTS

We thank our partners from the PRIVGEN project

, in

particular, D. Niyitegeka, G. Coatrieux and E. Genin,

for valuable discussions on watermarking and genetic

data sharing.

REFERENCES

Behjati S, T. P. (2013). What is next generation sequen-

cing? Archives of Disease in Childhood Education

and Practice Edition.

Bouslimi, D., Coatrieux, G., et al. (2016). Data hiding

in encrypted images based on predeﬁned watermark

embedding before encryption process. Signal Proces-

sing: Image Communication, 47.

Cherrueau, R.-A., Douence, R., and S

udholt, M. (2015).

A Language for the Composition of Privacy-

Enforcement Techniques. In IEEE RATSP, pages

1037 – 1044, Helsinki, Finland.

Ciriani, V., Vimercati, S. D. C. D., Foresti, S., et al. (2010).

Combining fragmentation and encryption to protect

privacy in data storage. ACM Trans. Inf. Syst. Secur.,

13(3):22:1–22:33.

Cousin, E., Deleuze, J.-F., and G

enin, E. (2006). Selection

of SNP subsets for association studies in candidate ge-

nes: . . . . BMC Genetics, 7:20.

Erlich, Y. and Narayanan, A. (2014). Routes for breaching

and protecting genetic privacy. Nature Reviews Gene-

tics, pages 409–421.

Gulcher, J. R., Kristjansson, K., Gudbjartsson, H., and

Stefnsson, K. (2000). Protection of privacy by third-

party encryption in genetic research in iceland. Euro-

pean Journal Of Human Genetics.

Homer, N., Szelinger, S., Redman, M., et al. (2008). Resol-

ving individuals contributing trace amounts of dna to

highly complex mixtures using high-density snp ge-

notyping microarrays. PLOS Genetics, 4(8):1–9.

Iftikhar, S., Khan, S., Anwar, Z., et al. (2015). Genin-

foguarda robust and distortion-free watermarking

technique for genetic data. PLOS ONE, 10(2):1–22.

Kantarcioglu, M., Jiang, W., Liu, Y., and Malin, B. (2008).

A cryptographic approach to securely share and query

genomic sequences. Trans. Info. Tech. Biomed.,

12(5):606–617.

Liina Kamm, Dan Bogdanov, S. L. et al. (2013). A new way

to protect privacy in large-scale genome-wide associ-

ation studies. Bioinformatics (Oxford, England).

Lu, W.-J., Yamada, Y., and Sakuma, J. (2015). Privacy-

preserving genome-wide association studies on clouds

using fully homomorphic encryption. BMC Medical

Informatics and Decision Making, 15(5):S1.

McLaren, P. J., Raisaro, J. L., Aouri, M., et al. (2016).

Privacy-preserving genomic testing in the clinic: a

model using hiv treatment. Genetics in Medicine,

18(8):814–822.

Sweenay, L. (2000). Simple demographics often identify

people uniquely. Carnegie Mellon. Data Privacy Wor-

king Paper 3.

Tang, H., Jiang, X., Wang, X., et al. (2016). Protecting

genomic data analytics in the cloud: state of the art

and opportunities. BMC Medical Genomics, 9(1):63.

Wang, R., Wang, X., Li, Z., Tang, H., et al. (2009). Privacy-

preserving genomic computation through program

specialization. In ACM CCS, pages 338–347, New

York, NY, USA. ACM.

Xie, W., Kantarcioglu, M., Bush, W. S., et al. (2014). Secu-

rema: protecting participant privacy in genetic associ-

ation meta-analysis. Bioinformatics, 30(23):3334.

Zhang, Y., Dai, W., Jiang, X., et al. (2015). Foresee: Fully

outsourced secure genome study based on homomor-

phic encryption. ”BMC Medical Informatics and De-

cision Making, 15(5):S5.

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