Bandwidth Efficient PIR from NTRU
Bandwidth Efficient PIR from NTRU
Yarkın Doröz1 , Berk Sunar1 and Ghaith Hammouri2
1
Worcester Polytechnic Institute
2
Crags Inc.
Abstract. We present a private information retrieval (PIR) scheme
based on somewhat homomorphic encryption (SWHE). In particular,
we customize an NTRU-based SWHE scheme in order to evaluate a specific class of fixed depth circuits relevant for PIR implementation, thus
achieving a more practical implementation. In practice, a SWHE that
can evaluate a depth 5 circuit is sufficient to construct a PIR capable of
retrieving data from a database containing 4 billion rows. We leverage
this property in order to produce a more practical PIR scheme. Compared to previous results, our implementation achieves a significantly
lower bandwidth cost (more than 1000 times smaller). The computational cost of our implementation is higher than previous proposals for
databases containing a small number of bits in each row. However, this
cost is amortized as database rows become wider.
Keywords: Private information retrieval, homomorphic encryption, NTRU.
1
Introduction
The problem of Private Information Retrieval (PIR) is one of the simplest yet
most useful concepts in cryptography. Simply put, a PIR scheme allows Alice to
store a database D at a remote server (Bob) with the promise that Alice can
retrieve D(i) without revealing i or D(i) to Bob. The notion of an information
theoretic PIR scheme was first introduced in [22] where the limits on Bob’s
knowledge of i were based on information theoretic arguments. In such a setting,
it can easily be shown that in a PIR scheme with a single server (single copy of
the database D) the only way to hide access to D in the information theoretic
setting is for Bob to send the entire database D back to Alice. Many solutions
were proposed in order to produce a secure information theoretic PIR scheme
when Alice can communicate with several servers storing a copy of D [22–24].
While these constructions are interesting from a theoretical point of view they
are difficult to achieve in a practical setting. For the remainder of this text we
focus only on single database PIRs.
As such Chor and Gilboa [25, 26] introduced the concept of computational
PIRs (cPIR). In cPIR, Alice is content to have the difficulty of Bob retrieving i
(or information about i) based on computational difficulty. That is Alice would
like Bob to face a computationally difficult problem in order to extract any
significant information about i or D(i). Since the introduction of cPIR many
2
schemes have been proposed. In [27] Kushilevitz and Ostrovsky presented the
first single server PIR scheme based on the computational difficulty of deciding
the quadratic residuosity of a number modulo a product of two large primes.
Other cPIR constructions include [28] which is based on the computational
difficulty of deciding whether a small prime p divides φ(m) for any composite
integer m of unknown factorization where φ() denotes Euler’s totient function.
In [29] another cPIR scheme was presented that generalizes the scheme in [28]
while using a slight variation on the security assumption. Most notably, the
construction in [29] achieves a communication complexity of O(k + d) where k
is the security parameter satisfying k > log(N ), N is the database size, and
d is the bit-length of the retrieved data. In [7] Lipmaa presented a different
yet quite interesting cPIR scheme that leverages a (length-flexible) additively
homomorphic public-key encryption scheme and provides better communication
complexity performance. Later in [3], an efficient PIR scheme is produced using a
partially homomorphic encryption algorithm. This was later followed by a lattice
based cPIR construction by Aguilar-Melchor and Gaborit [30].
In 2007 the computational practicality of PIRs was raised by Sion and Carbunar [20] who concluded that no existing construction is as efficient as the
trivial PIR scheme.The authors observe that any computational PIR scheme
that requires one or more modular multiplications per database bit cannot be
as efficient as the trivial PIR scheme. Later, Olumofin and Goldberg [21] revisited the performance analysis and found that the lattice-based PIR scheme by
Aguilar-Melchor and Gaborit [30] to be an order of magnitude more efficient than
the trivial PIR in situations that reflect average consumer internet bandwidth.
In all these constructions the challenge has been to find an efficient scheme
based on a difficult computational problem. The aforementioned schemes utilize
a variety of approaches and a diverse set of tools to construct cPIR schemes.
However, it has always been clear that given a fully or somewhat homomorphic
encryption (SWHE or FHE) scheme achieving a cPIR construction would be
conceptually as simple as carrying out normal information retrieval. Although
fully homomorphic encryption schemes have been introduced in 2009 [1] efficiency has been the biggest hindrance preventing any practical implementation.
As such, FHE schemes have yet to be shown to be useful in progressing a practical realization of a private information retrieval. However, a number of new
FHE schemes [5, 10, 4, 6, 11] and optimizations such as modulus and key switching [8], batching and SIMD optimizations [9] have been introduced in just the
last few years which improved the efficiency of FHE implementation roughly by
two orders of magnitude per year.
Our Contribution. Motivated by these advances, in this work we take a first
step towards using a SWHE scheme along with optimizations developed for leveled SWHE implementation to construct an efficient cPIR. We construct a rather
simple implementation of a PIR scheme from a batched leveled SWHE implementation based on the NTRU encryption scheme. Our scheme has excellent
bandwidth performance compare to previous implementations (more than 1000
times smaller). The computational cost of our implementation is higher than
3
previous proposals for databases containing a small number of bits in each row.
However, this cost is amortized as the database rows become wider.
2
Homomorphic Encryption Based PIR Schemes
In this section we briefly survey 3 representative cPIR schemes constructed out
of homomorphic encryption schemes most relevant to our proposal. We note that
this survey is only intended to provide a basis for later comparison.
2.1
Kushilevitz-Ostrovsky PIR
At the essence of the K-O scheme [2] is the use of a secure homomorphic operations and the idea of conceptually storing the database as a matrix. To elaborate,
we can think of Bob as having a database D of size 2h with each location containing a single bit (this can easily be extended for longer strings). Bob then
stores D in a matrix M of size 2h/2 × 2h/2 . For any location i in the database D,
this process can be done by using the first h/2 bits of i to represent the number
of the row in M and the last h/2 bits of i to represent the number of the column
in M where i will be placed. Now for Alice to recover the entry D(i), she will
take the first h/2 bits of i encode them into a one hot encoding A and carry out
the same process for the lower h/2 bits of i to produce B. Finally, Alice uses
a partially homomorphic encryption scheme E to encrypt each bit in A and B.
Thus Alice sends to Bob (E(A0 ) . . . E(Ah/2−1 ), E(B0 ) . . . E(Bh/2−1 )). With this
information Bob can now carryout some homomorphic operations between the
encrypted bits sent by Alice and the data stored within the matrix M in order
to produce an encrypted output which encodes the bit D(i) and can then be
h/2
h/2
sent to Alice for decryption. The matrix is of
= N and therefore
√size 2 × 2
the communication complexity becomes O( N ).
2.2
Boneh-Goh-Nissim (BGN) Scheme
The BGN cryptosystem is a partially homomorphic encryption scheme [3] capable of evaluating 2-DNF expressions in encrypted form. For example, given two
encryptions of messages, we can obtain an encryption of the sum of the messages
without decryption or compromising privacy. Indeed, the cryptosystem remains
semantically secure. Being (in part) based on the Paillier cryptosystem, BGN
inherits its additive homomorphic properties. Moreover, with the clever introduction of pairings, BGN is capable of homomorphically evaluating one level of
multiplication operations.
The BGN algorithm constructs a homomorphic encryption scheme using finite groups or composite order that support bilinear maps. The construction outlined in [3] uses groups over elliptic curve where homomorphic additions translate into elliptic curve point addition and homomorphic multiplication translates
into a pairing operation. Leveraging the single multiplication afforded by pairing operation Boneh, Goh and Nissim also manage to reduce the communication
4
complexity
in √
the basic step of the Kushilevitz-Ostrovsky PIR protocol from
√
O( N ) to O( 3 N ). In contrast, the computational efficiency of BGN (for the
server side PIR computation) scheme lies the pairing operation. Guillevic [18]
developed optimized implementations to support BGN which reveals that parings over composite order elliptic curves are far less efficient than parings over
prime order curves and also require significantly larger parameter sizes to reach
the same security level.
2.3
Aguilar-Melchor-Gaborit’s Lattice Based PIR
Most of the single server cPIR schemes rely on costly algebraic operations with
large operands such as modular multiplications [32, 2, 33], or pairing operations
on elliptic curves [3] to realize the homomorphic evaluations. In contrast, the
PIR scheme by Aguilar-Melchor and Gaborit [30, 31] makes use of a new lattice based construction replacing costly modular operations, with much cheaper
vector addition operations in lattices. The security is based on the differential
hidden lattice problem, which they relate like in many lattice based construction
to NP-complete problems in coding theory. Via this connection the scheme is
also related to the NTRU scheme [13].
Very briefly, the PIR schemes works as follows. The scheme utilizes a secret
random [N, 2N ] matrix M of rank N over a field Zp which is used to generate a
set of different matrices obtained by multiplication by invertible random matrices. These matrices are disturbed by the user by the introduction of noise in half
of the matrix columns to obtain softly disturbed matrices (SDMs) and hardly
disturbed matrices (HDMs). To retrieve an element from the database the client
sends a set of SDMs and one HDM to the PIR server. The PIR server inserts
each of its elements in the corresponding matrix with a multiplicative operation
OP and sums all the rows of the resulting matrices collapsing the PIR server
reply to a single noisy vector over Zp .
While they proposed full fledged protocol and implementation [30, 31], their
analysis was limited to server-side computations on a small database consisting
of twelve 3 MByte files. Later Olumofin and Goldberg [21] performed extensive
experiments with a broad set databases sizes under realistic network bandwidth
settings determining that the lattice based Aguilar-Melchor and Gaborit PIR
scheme is one order of magnitude more efficient than a simple PIR.
3
From SWHE to PIR
Consider a database D with |D| = 2` rows. Clearly ` index bits are sufficient to
address all rows. Assume the data bit contained in row i is denoted by Di . We
may retrieve an element of D with given index x ∈ {0, 1}` which holds Dx by
computing:
X
f (x) =
(x = y)Dy (mod 2) ,
(1)
y∈[2` ]
5
Q
where the bitwise comparison (x = y) may be computed as i∈[`] (xi + yi + 1).
Here [`] = {0, 1, . . . , ` − 1} for ` > 0 and [`] = {} otherwise. The function of
the inner loop is to check if each bit of the given x matches the corresponding
bit of the y value currently processed. The boolean result is multiplied with the
current data value Dy and added to the sum. All of the summed terms except
the one where there was a match becomes zero and therefore does not affect the
result. Therefore, f (x) = Dx .
This arithmetic retrieval formulation allows us to build PIRs and sPIRs. In
this case the index value x is in encrypted form. Therefore, the database curator
does not know which row is read from the database. We wish the curator to
still be able to retrieve and serve the requested row. The data in the row itself
can also be in encrypted form in which case the protocol is referred to as a
symmetric PIR or sPIR in short. In this setting, if the index x is encrypted using
a homomorphic encryption scheme E we may evaluate f (x) homomorphically.
From the formulation of f (x) we need E to be able to compute a large number
of homomorphic additions (XORs) O(2` ) and a small number of multiplications
(ANDs) ` and ` + 1 if the rows are encrypted3 .
4
Picking the SWHE Scheme
To build a PIR for a database of size 2` as described in Section 3 we need an
efficient SWHE instantiation that can evaluate a circuit of depth dlog2 (`)e. In
practice a depth 5 or 6 circuit will suffice since that will give us an ability to
construct a PIR for a database of size 232 and 264 , respectively.
For this we make use of the modified NTRU scheme [13] introduced by Stehlé
and Steinfeld [12] with a number of optimizations introduced to this construction
by Lopez-Alt, Tromer and Vaikuntanathan [11] to turn Stehlé and Steinfeld’s
scheme into a full fledged fully homomorphic encryption scheme. Here we only
need to support a few levels and therefore the full Lopez-Alt, Tromer and Vaikuntanathan scheme is not needed. Stehlé and Steinfeld [12] formalized the security
setting and reduced the security of their NTRU variant to the ring learning with
error (RLWE) problem. More specifically, they show that if the secret polynomials are selected from the discrete Gaussian distribution with rejection then
the public key is indistinguishable from a uniform sample. Unfortunately, the
reduction does not carry over to the fully homomorphic setting since relaxation
of parameters e.g. a larger modulus is needed to accommodate the noise growth
during homomorphic evaluation as noted in [11]. We next summarize our instantiation of the scheme in [12] in a way that supports restricted homomorphic
evaluations but does not require all the machinery of [11].
We require the ability to sample from a probability distribution χ on Bbounded polynomials in Rq := Zq [x]/(xn +1) where a polynomial is “B-bounded”
if all of its coefficients lie in [−B, B]. For example, we can sample each coefficient
3
Note that we restricted the database entries Di to be bits but a w-bit entry can also
easily be handled by considering w parallel and independent function evaluations.
6
from a discrete Gaussian with mean 0 and discard samples outside the desired
range. Each AND gate evaluation incurs significant noise growth and therefore
we use the modulus reduction technique introduced by Brakerski, Gentry and
Vaikuntanathan [10]. We assume we are computing a leveled circuit with gates alternating between XOR and AND and modulus reduction taking place after each
AND level. We use a decreasing sequence of odd prime moduli q0 > q1 > · · · > qd
where d is the depth of the PIR circuit. In this way, the key (public and evaluation keys) can become quite large and it remains a practical challenge to manage
the size of this data and handle it efficiently. For this implementation we specialize the prime moduli qi by requiring qi |qi+1 as was proposed in [16]. This
allows us to eliminate the need for key switching and to reduce the public key
size significantly. Also in this implementation we do not use relinearizations as
proposed in [11] since we are in a single user setting and we have a shallow
well structured circuit ( a perfect binary tree) to evaluate. This will significantly
improve the efficiency of implementation since relinearization is an expensive
operation [16]. The primitives are as follows:
– KeyGen: We choose a decreasing sequence of primes q0 > q1 > · · · > qd and
a polynomial Φm (x), the m-th cyclotomic polynomial of degree n = ϕ(m).
For each i, we sample u(i) and g (i) from distribution χ, set f (i) = 2u(i) + 1
−1
and h(i) = 2g (i) f (i)
in ring Rqi = Zqi [x]/hφ(x)i. (If f (i) is not invertible
in this ring, re-sample.)
– Encrypt: To encrypt a bit b ∈ {0, 1} with a public key (h(0) , q0 ), random
samples s and e are chosen from χ and compute the ciphertext as c(0) =
h(0) s + 2e + b, a polynomial in Rq0 .
– Decrypt: To decrypt the ciphertext c with the corresponding private key
i
f (i) = f 2 ∈ Rqi , multiply the ciphertext and the private key in Rqi then
retrieve the message via modulo two reduction: m = c(i) f (i) (mod 2).
(0)
(0)
– XOR: For two ciphertexts c1 = Encrypt(b1 ) and c2 = Encrypt(b2 ) then their
homomorphic XOR is evaluated by simply adding the ciphertexts Encrypt(b1 +
(0)
(0)
b2 ) = c1 + c2 .
– AND: Polynomial multiplication is realized in two steps. We first compute
(i−1)
(i−1)
c̃(i−1) (x) = c1
· c2 j (mod φ(x))
m and then perform a modulus reduction
operation as c̃(i) (x) = qq01 c̃(i−1) (x) where the subscript 2 on the rounding
2
operator indicates that we round up or down in order to make all coefficients
equal modulo 2.
4.1
Concrete Setting
To instantiate the Stehlé Steinfeld variant of NTRU for depth d we need to
pick a large enough q0 value to reduce the modulus d times. For instance, for a
selection of B = 2 and if we cut by 24 bits in each iteration we need at least 200
bits. For such a q parameter we can then select n based on the Hermite factor.
The Hermite factor was introduced by Gama and Nguyen [14] to estimate the
7
hardness of the shortest vector problem (SVP) in an n-dimensional lattice L and
is defined as
||b||
γ 2n =
1
vol(L) 2n
where ||b|| is the length of the shortest vector or the length of the vector for
which we are searching. The authors also estimate that, for larger dimensional
lattices, a factor δ n ≤ 1.01n would be the feasibility limit for current lattice
reduction algorithms. In [15], Lindner and Peikert gave further experimental
results regarding the relation between the Hermite factor and the recovery time
as t(γ) := log(T (γ)) = 1.8/ log(γ) − 110. For instance, for γ n = 1.0066n , we need
about 280 seconds on an AMD Opteron running at 2.5 Ghz [15]. Since we are
using a construction based on NTRU we need to determine the desired Hermite
factor for the NTRU lattice. Coppersmith and Shamir in [19] show that an
attacker would gain useful information with a lattice vector as close as norm q/4
to the original secret key vector. Therefore we take ||b|| = q/4 and vol(L) = q n
√
and compute the Hermite factor for the NTRU lattice as γ = ( q/4)1/(2n) .
To select parameters we also need to consider the noise growth. Since we no
longer use relinearization, the powers of the secret key will grow exponentially
through the levels of evaluation. To cope with the growth we use the modulus
reduction as described in Section 4. Following the noise analysis of [16] (Section
i
d
5) we can express the correctness condition as ||c2 f 2 ||∞ < qd /2|| assuming we
are evaluating a depth d circuit. Also note that instantiation we fix χ to choose
from {−1, 0, 1} with probabilities {0.25, 0.5, 0.25}, respectively. With modulus
d
d
reduction rate of κ ≈ qi+1 /qi the following equation holds c2 f 2 = (. . . ((c2 κ +
d
p1 )2 κ + p2 )2 . . . κ + p2i )f 2 . In Table 1 we computed the Hermite factor and
supported depth for various sizes of q0 and n for our scheme.
Table 1. Hermite factor and supported circuit depth (γ, d) for various q and n.
n
213
214
215
5
log2 (q)
512
640
768
1024
1280
(1.01083, 5) (1.0135, 5) (1.0162, 6) (1.0218, 6) (1.0273, 7)
(1.00538, 5) (1.0067, 5) (1.0081, 6) (1.0108, 6) (1.0135, 6)
(1.00269, 5) (1.0033, 5) (1.0040, 6) (1.0054, 6) (1.0067, 6)
The NTRU based PIR Protocol
In our encryption scheme we are able to batch additional information to the
ciphertext polynomials. This allows us to perform retrieval using two different
query mechanisms:
Bundled Query one query is used to to retrieve data stored at different rows
of the database (different indicies are queried).
8
Single Query the query retrieves data from a single row (a single index) but
processes more indices at a time during the PIR server computation.
Next we explain an FHE optimization technique named batching and show how
it gives us the two query methods.
Batching. Batching was introduced by Smart and Vercauteren [8, 9]. It allows us to evaluate a circuit on multiple independent data inputs simultaneously by embedding them into the same ciphertext. The independent data inputs are encoded to form special binary polynomials that are used as message
polynomials. Addition and multiplication of these the message polynomials has
the effect of evaluating XOR and AND operations on the packed message bits.
The encoding is achieved using the Chinese Remainder Theorem. First we set
Rq0 = Zq0 /hΦm (x)i, where Φm (x) is defined as the mth cyclotomic polynomial.
The cyclotomic polynomial
QΦm (x) is factored into equal degree irreducible polynomials over F2 Φm (x) = i∈[ε] Fi (x). where λ = deg(Fi ) is the smallest integer
that satisfies m|(2λ − 1). A message polynomial m(x) in the residue space is
represented as mi = m(x) (mod Fi (x)). Therefore; given a message bit vector
m = {m0 , m1 , m2 , m3 . . . , mε } we may compute the corresponding message polynomial using inverse CRT m(x) = CRT−1 (m). Using these special formed messages, we can perform bit level AND and XOR operations: mi · m0i = m(x) · m0 (x)
(mod Fi (x)) and mi ⊕ m0i = m(x) + m0 (x) (mod Fi (x)).
Bundled Query. The batching technique allows us to embed multiple indices
into a query ciphertext and thereby facilitate
retrieval of multiple database enP
Q
tries. First recall our PIR function
y∈[2` ] [ i∈[`] (xi + yi + 1)]Dy , which we
will now evaluate on encrypted x and y values. Using the batching technique
we may evaluate ε retrievals with indices β[1], . . . , β[ε] simultaneously. First we
form their bit representation as:
β[1] = (β`−1 [1] β`−2 [1]
β[2] = (β`−1 [2] β`−2 [2]
..
..
..
.
.
.
β[ε] = (β`−1 [ε] β`−2 [ε]
...
...
β0 [1])
β0 [2])
..
.
...
β0 [ε])
Using the columns of the bit matrix on the RHS, we can compute the batched
polynomial versions of the index bits β̃i (x) as:
β̃i (x) = CRT−1 (βi [1], βi [2], . . . , βi [ε])
Later, these polynomials are encrypted as: ξi (x) = h(x)si (x) + 2ei (x) + β̃i (x)
for i ∈ [`]. The query Q = [ξi (x), . . . , ξ`−1 (x)] is then send to the PIR server. In
order to perform parallel comparisons vector row index bit {yi , yi , . . . , yi } should
also converted into a polynomial representation using inverse-CRT. Since we are
dealing with bits yi = {0, 1}, the inverse-CRT will result in {0, 1} polynomials,
and thus yi (x) = yi . This is true fordata Dy as well. Then, we can rewrite
P
Q
the PIR equation as: r(x) = y∈[2` ]
i∈[`] (ξi (x) + yi (x) + 1) Dy (x). Given
9
that yi (x) has small coefficient, i.e. 1 or 0, the additions are done over the least
coefficient term in the polynomial. Furthermore, having Dy (x) = {0, 1} we may
skip the product evaluations unless Dy (x) = 1. Once r(x) is homomorphically
evaluated simultaneously over the ` ciphertexts, the response (a single ciphertext) R = r([ξ0 (x), . . . , ξ`−1 (x)]) is sent back to the PIR client. The ciphertext
response is first decrypted and the individual data entries are recovered using
modular reductions: Di = dec(r(x)) (mod Fi (x)).
Single Query. In the single query mode we will also perform batching as in
the Bundled Query mode. However, here we will place the same index into all
index slots. The resulting polynomials are encrypted as before giving us a query
Q = [ξi (x), . . . , ξ`−1 (x)]. Though this is similar to the Bundled Query, the PIR
server side computation is handled quite differently. For parallel comparisons we
batch the row bits of yi and Dy as well:
yi (x) = CRT−1 {yi [1], . . . , yi [ε]},
Dy (x) = CRT−1 {Dy [1], . . . , Dy [ε]}.
These conversions are done on-the fly and are not precomputed. Working in
modulo 2 arithmetic makes the evaluations sufficiently fast and easy such that
it only adds a small overhead. Although precomputation is an option, storing
converted message polynomials would take extra space. The comparison equation
will stay the same with the Bundled Query, but yi (x) and Dy (x) will now binary
polynomials. Therefore, we require polynomial addition inside the product and a
polynomial multiplication with Dy (x). Since in each iteration we are comparing ε
indecies simultaneously we can process the database ε times faster. This speedup
comes at a price where each iteration need to carryout a multiplication by the
polynomial representation of the batched Dy .
The response ciphertext is first decrypted and then reduced to recover the
evaluation bits as before: zi = dec(r([ξ0 (x), . . . , ξ`−1 (x)])) (mod Fi (x)). In a
Single Query each zi refers to a subsection of the summationP
therefore to compute
the overall result we perform a final bit summation Dy = i∈ε zi mod 2.
6
Performance
We implemented the proposed PIR protocol with both the Single and Bundled
Querying modes in C++ where we relied on Shoup’s NTL library version 6.0
[17] for the lattice operations. Table 2 shows minimal parameters to support
d
various evaluation depths. Each depth can support upto 22 entries, e.g. d = 5
can support 4 Billion entries. The parameter ε denotes the number of message
slots that we can bundle. The query and response sizes are given in Table 2
without normalization by ε. In the Bundled Query mode sizes may be normalized
with ε to determine the bandwidth per query. In Table 3, we present the time
performance for query processing. The reported times are normalized per row
of the database and per query. The time is split into two components: the time
required to compare the encrypted index to the index of the currently processed
row, and the time required to add the data in the current row to the summation.
10
Table 2. Polynomial parameters and Query/Response sizes necessary to support various database sizes N .
max N
4 Billion
65536
256
(log q, n)
(512, 16384)
(250, 8190)
(160, 4096)
ε Query Size (MB) Response Size (KB)
1024
32
784
630
3.9
154
256
0.625
44
While the computation cost in comparison is quite high we should note that
we are paying primarily for the index comparison. In the Bundled Query case,
once the index comparison is completed we may simply reuse the comparison
result and only compute an addition operation for each additional bit in the
same database entry. In this sense, our results are similar to the other lattice
based PIR construction by Melchor and Gaborit [30]. The index comparison
may be considered as a one time overhead to be paid for each row that would
be amortized as database rows get wider. Still due to the large vector sizes data
aggregation will be rather slow. For instance; in a Bundled Query with d = 4
and 1 GBytes of data in a row, the processing time will be about 8 times slower
than a Kushilevitz and Ostrovsky implementation as given in [21].
Table 3. Index comparison and data aggregation times per entry in the database for
(d, ε) choices of (5, 1024), (4, 630) and (3, 256) on Intel Pentium @ 3.5 Ghz.
Bundled Query (msec) Single Query (msec)
Depth (d)
5
4
3
5
4
3
Index comparison 4.45 0.71
0.31
4.56
2.03 1.29
Data aggregation 0.22 0.09
0.04
37
7.45 3.40
What we loose in computational efficiency, we make up for in terms of bandwidth. In Table 4, we give Complexity and Query size comparisons. As before, N
is the size of the database and α is the ciphertext size that differs in each scheme
4
. In the Bundled Query case, for instance, the query is formed by ` = 2d = 32
ciphertexts each made of Mbytes. By normalizing with ε index retrievals in a
single query, per retrieval we are paying about 32 Kbytes. The query size of our
scheme is smaller by a factor of 1024, 1200, and 3072 when compared to BGN,
Melchor-Gaborit and Kushilevitz-Ostrovsky, respectively.
Finally, we would like to point out that for all practical purposes the size α of
the ciphertexts in the query and response can be considered almost independent
of the database size. Therefore, the size of the ciphertext, i.e. α is very mildly
effected when the database size is increased. Indeed, as seen in Table 4 when the
4
For [30], we used the given size of 37.5 MByte for 20,000 entries since it does not
provide a complexity. The size will grow significantly when N goes to 232 .
11
table size is grown from 256 entrees to 216 entries, the ciphertext size grows only
about by 1.26 times in the bundled case.
Table 4. Comparison of query sizes for databases upto 232 , 216 and 28 entries. Bandwidth complexity is given in the number of ciphertexts; α denotes the ciphertext size.
BW
α
Compl.
d
=
5
d
=4
√
Boneh-Goh-Nissim
α √N
6144
6144
Kushilevitz-Ostrovsky α N
2048
2048
Ours (Single)
α log N 8388608 2047500
Ours (Bundled)
α log N 8192
3250
Query Size
d=3
d=5
d=4 d=3
6144
96 MB 384 KB 24 KB
2048
32 MB 128 KB 8 KB
655360 32 MB 249 KB 80 KB
2560
32 KB
406 B 320 B
Acknowledgments
Funding for this research was in part provided by the US National Science Foundation CNS Awards #1117590 and #1319130.
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