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Lattices in Cryptography Andy Guo Guoandy86@Gmail.Com University of Connecticut OpenCommons@UConn Honors Scholar Theses Honors Scholar Program Spring 5-3-2019 Lattices in Cryptography Andy Guo [email protected] Follow this and additional works at: https://opencommons.uconn.edu/srhonors_theses Recommended Citation Guo, Andy, "Lattices in Cryptography" (2019). Honors Scholar Theses. 612. https://opencommons.uconn.edu/srhonors_theses/612 Lattices in Cryptography Andy Guo May 3, 2019 Abstract The computers that exist throughout the world today, rely on two val- ues, 0 and 1 to work and run. Quantum computers, on the other hand, uses qubits which are quantum objects that can be in either a 0 or 1 state, or even a superposition of 0 and 1 state. Quantum computers do not nec- essarily make traditional computer algorithms run faster, but rather lets us come up with new algorithms specifically for quantum computers that will be faster than traditional computers. Quantum computer algorithms are designed to make full use of superpositional states. These quantum computers will be able to break commonly used public key cryptosystems such as RSA, DSA, and other practical ones as well. Some physicists pre- dict that quantum computers will become powerful enough in the next 10 years to break these existing public key cryptosystems. As a result, a new field of research in post quantum cryptography is on the rise. One of the most well known and researched post-quantum cryptography schemes are ones that revolved around lattices. 1 Introduction The computers that exist throughout the world today, rely on two values, 0 and 1 to work and run. Quantum computers, on the other hand, uses qubits which are quantum objects that can be in either a 0 or 1 state, or even a superposition of 0 and 1 state. Quantum computers do not necessarily make traditional computer algorithms run faster, but rather lets us come up with new algorithms specifically for quantum computers that will be faster than traditional computers. Quantum computer algorithms are designed to make full use of superpositional states. The quantum computations in the algorithm are done in parallel on superpositions of exponentially many inputs [23]. In other words, the quantum computer is able to go down every path simultaneously, and quantum computer algorithms exploit that to their advantage. The superpositional states of each qubit allows it to represent multiple numbers or values simultaneously. The value of a qubit is not determined till the end of the calculation when it has solved the problem. These quantum computers will be able to break commonly used public key cryptosystems such as RSA, DSA, and other practical ones as well [29]. Some physicists predict that quantum computers will become powerful enough in the 1 next 10 years to break these existing public key cryptosystems [5]. As a result, a new field of research in post quantum cryptography is on the rise [5]. As of right now, there are four research areas in the post-quantum cryptosystems field that can stop quantum computers: code-based, hash-based, lattice-based, and multivariate public key cryptosystems [7]. Out of the four, one of the most well known and researched post-quantum cryptography schemes are ones that revolved around lattices [21]. Lattice cryptography not only is a well known post-quantum cryptography scheme, it also can be used for fully homomorphic encryption. Fully homomorphic encryption or FHE has been sought after for many years by cryptographers [12], as it has been seen as an elusive goal that can solve the world's problems with security and trust. A homomorphic encryption means that computations can be done directly to the encryption of a message instead of decrypting the ciphertext and then encrypting it again. This would save time and allows confidential data to be computed in unsafe environments and still remain confidential. A system like this would help the flow of data and information remain safe and secure. In unsafe and untrusted places, one would not need to worry about the environment taking their data. The data will not become vulnerable as it will never be decrypted to the original data, as more computation can be done directly to the ciphertext. Since FHE is still an encryption scheme, it would be ideal that it is based off an encryption scheme that is known to be hard to break. As a result, a fully homomorphic encryption scheme has been built from learning from errors assumption or LWE from the works of Gentry [12]. The security of this scheme is not only based the assumed hardness of LWE but also the sparse subset sum problem. The sparse subset sum problem is defined as given a set of integers A, and two integers, t and M, find a sparse subset of the set that sums up to t mod M. The sparse subset sum problem is hard and only gets harder when the given set is larger [17]. This paper will discuss what makes lattices a good foundation for building quantum computer resistant cryptography schemes. A lattice is a regular arrangement of points in n-dimensional Euclidean space. The set of points can be described by using a set of n linearly independent vec- n tors, v1; :::; vn 2 R . A lattice is formally defined as the set of all integer com- binations of those n linearly independent vectors. These linearly independent vectors are known as the basis of lattice [26]. The lattice L can be represented as n X L = f αivi j α 2 Zg: i=1 This shows that L is the integer combinations of the n linearly independent vectors of the basis. The lattice is not the vector space spanned by the basis, as the span of the basis is the set of all real linear combinations of the n vectors, whereas the lattice only has the set of all combinations that has an integer coefficient to it. Due to this, a lattice is a discrete set, meaning that the points in the lattice cannot be too close to one another. There is a minimum distance between points in each lattice, where the minimum distance is greater than 2 0 [20]. This makes it so that lattice problems cannot be solved with simple linear algebra. Another important thing to note about the basis of a lattice is that it is not unique. There can be two bases that could end up generating the same lattice. Two bases are equivalent if multiplying one of the bases by a unimodular matrix will make the two equivalent UB1 = B2; where U is the unimodular matrix. An unimodular matrix is any square matrix with integer entries that has a determinant of ±1. Now that a lattice has been defined, lets go over some properties it and its basis has. The rank of L is the number of vectors in B, and the dimension of the lattice is just the dimension of each vector. If the rank is equal to the dimension, then the lattice is called a full ranked lattice. The dimension of a lattice is the number of elements in each column vector. All lattices has a dual to them. Given a lattice, L, the dual of L, is L∗. L∗ is a lattice given by the set of all vectors y 2 Rn and the set of vectors x 2 L such that for all x 2 L; hx; yi 2 Z [26]. The dual of a lattice is also equivalent to the inverse of the transpose of the lattice, L∗ = (LT )−1. From that, it can be shown that the determinant of L∗ is the reciprocal of the determinant of L. The dual of a lattice is important as it can be used to solve some of these hard lattice problems. A basis that describes L can also generate a fundamental parallelepiped. The fundamental parallelepiped of the basis is defined as n X P (B) = f xivi j xi 2 [0; 1)g: i=1 If the choice for the basis that describes L is clear, then P(B) can be rewritten as P(L), which still means the fundamental parallelepiped as generated by the basis that describes L. The volume of the fundamental parallelepiped of the basis is the same as the determinant of L. The volume of the parallelepiped is also the same for two different bases if they describe the same lattice. That is not the only way to calculate det(L). It can also be calculated from finding the absolute value of the determinant of a matrix which is made of the basis vectors that describes L. Geometrically, the determinant of the lattice shows the inverse density of lattice points in space. As mentioned before, a lattice can be described by multiple bases, and each of those bases will have their own respective fundamental parallelepipeds. The space can be \tiled" with the fundamental parallelepiped, where each lattice point is given a copy of the fun- damental parallelepiped [21]. The fundamental parallelepiped is also essential in finding the modulo of a point x 2 Rn. x mod P (L) is the unique point y 2 P (L) such that y − x 2 L [26]. The Gram-Schmidt orthogonalization of a basis B = [v1; :::; vn] can be writ- ∗ ∗ ∗ ∗ ten as B . B = [v1 ; :::; vn] is defined as, i−1 ∗ X ∗ vi = vi − µijvj ; j=1 3 where µij is, ∗ hvi; vj i µij = ∗ ∗ ; for 1 ≤ j ≤ i ≤ n: hvj ; vj i The Gram-Schmidt orthogonalization projects the vector vi of B onto a space orthogonal to the space spanned by v1; :::; vi−1. However, the vectors produced by the Gram-Schmidt procedure does not necessarily produce a lattice as the vectors may not all be integer combinations of the basis, and as a result cannot be a lattice.
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