How blockchain can fight back the quantum computing security threat

How blockchain can stand against the quantum computing security threat

Quantum computing poses a threat to many elements of digital security. Is this the case for blockchain solutions such as Ericsson Data Centric Security? We look at the case.

Cryptography is more or less based on mathematical functions called algorithms. These algorithms are designed in a such way that the data is effortless to calculate in one direction but hard to calculate in the other direction. So given x, it is effortless to find f(x)= y, but given y it is supposed to be hard to find x.

Quantum computing and insecure functions

The solution to this problem is plain: stop using the freshly insecure mathematical functions and instead use mathematical functions known to be immune from a quantum computing threat.

So far so good. But what are these secure functions?

Hash functions and security

We can embark with a mathematical function called a hash function, which remains secure if faced with a quantum computing threat, as long as it fulfills certain criteria. When these criteria are fulfilled, hash functions are unique and create fixed-sized, irreversible fingerprints of data.

In general, a cryptographic hash function is considered secure if—when fed an arbitrary lump of data—it comes back a truly random value for every different query while a repeated query comebacks the same random value each time. For example, you could feed “Security is significant” into the hash function, and the output could be “Ten.” But if you feed “Security is a must,” you would get back, say, “13.” Then if you comeback to “Security is significant,” the hash function must once again comeback “Ten.”

Today’s secure hash algorithms are considered among experts to be SHA-2 and SHA-3.

However, there are certain caveats that a hash function needs to fulfill in order to be perceived secure.

Pre-image resistance

Very first, the hash function must have “pre-image resistance.” In principle, it must not be possible to go rearwards from an response to the related question. This means that if the hash function output is “Ten,” to derive that the original feed was “Security is significant” must not be possible.

2nd pre-image resistance

2nd, the hash function needs to have “2nd pre-image resistance.” In principle, it should not be possible from looking at the feed “Security is significant” to determine that the feed ” Security is always significant” will produce the same result. This means that if a hash function is fed with “Security is always significant” it should not come back “Ten”—which is the result from the feed “Security is significant.”

Collision resistance

Third, the hash function must have collision resistance. This is similar to 2nd pre-image resistance. It must not be possible to lightly find two messages that have the same hashed result. Even however collisions will exist simply due to the limited range of outputs versus the infinite range of inputs, it must not be possible to lightly derive colliding messages.

If both “Security is significant” and “Security is always significant” resolve to “Ten,” it must not be possible to conclude this result independently. It must take a tremendously (read, improbable) amount of brute force work to detect that the two inputs resolve to the same hashed output.

Ericsson Data Centric Security and quantum computing

So where does this leave the Ericsson Data Centric Security solution?

This solution is based on a blockchain architecture, with two fundamental and crucial parts that make it immune to quantum computing threats: the cryptographic algorithm (SHA2-256) and the Merkle signature scheme also known as a binary hash tree.

The Merkle signature scheme

The Merkle signature scheme is based on a mathematical hierarchical hash tree structure, which has the form of a tree in which the lower branches have the largest amount of leaves. The higher in the tree, the smaller the branches and the fewer leaves on each branch, until the top is reached.

Native hash data is fed into the bottom of the tree, as source input. The architectural functionality is then to concatenate all hash data from the lower layers into next layer of branches until the top of the tree is reached, where a root hash is produced. The Merkle tree works in a dedicated and held time slot of one 2nd. This means that each Merkle tree schema, with the associated concatenation activity and the creation of a tree-top root hash, is carried out and processed every 2nd.

Thereafter the Merkle tree schema is ripped down, for the next 2nd to become rebuilt again and again—and so on for every 2nd into the future. What is significant to acknowledge in context to the Merkle tree is that the hash tree is secure as long as it uses a secure hash function in its architecture that is resistant to pre-images and 2nd pre-images as, for example, SHA2-256 or above.

Unique blockchain security

Another fact that makes the Ericsson Data Centric Security blockchain solution unique is the way the blockchain is securely created and processed every 2nd. Since each root hash is time-stamped and cryptographically interlinked with the previous time slot hash value, the digital blockchain ledger becomes immutable for rearwards switches in time, as well as for other general tampering.

So ultimately, against a quantum computing threat, a “ideal” hash function of output size “n” bits still offers strong resistance as long it is above one hundred twenty eight bits—which means two to the (n/Two) power or above, for example, with SHA2-256, a 256-bit output. In this case, the best quantum computer would still need two to the 128th power of simultaneous operations (that is, with current technology too many to be feasible by a fat margin) to break pre-image resistance. Further a possible attack has to be executed within one 2nd for each leave in the binary hash tree at the same time in order to become close knowing what information is being hashed, concatenated and processed, which is today know to be unlikely even for furture quantum computers, which makes Ericsson Data Centric Security the ideal choice for thwarting quantum computing threats.

Mads Becker Jorgensen

Mads works as Technical Product Manager in the security product line at Ericsson in Kista, Stockholm. Mads has a background as an Information Security Specialist with over fifteen years field work, both palms on and conceptual, as a consultant via various international security companies. Mads’ concentrate is on the value of holistic security and especially how to prove and measure it by working with people processes and technology. Mads’ beloved question is “If you can´t measure the value of security, how do you indeed know it works, and how do you prove it when you need it?” The response is . you simply don´t.

How blockchain can stand against the quantum computing security threat

How blockchain can fight back the quantum computing security threat

Quantum computing poses a threat to many elements of digital security. Is this the case for blockchain solutions such as Ericsson Data Centric Security? We look at the case.

Cryptography is more or less based on mathematical functions called algorithms. These algorithms are designed in a such way that the data is effortless to calculate in one direction but hard to calculate in the other direction. So given x, it is effortless to find f(x)= y, but given y it is supposed to be hard to find x.

Quantum computing and insecure functions

The solution to this problem is elementary: stop using the freshly insecure mathematical functions and instead use mathematical functions known to be immune from a quantum computing threat.

So far so good. But what are these secure functions?

Hash functions and security

We can embark with a mathematical function called a hash function, which remains secure if faced with a quantum computing threat, as long as it fulfills certain criteria. When these criteria are fulfilled, hash functions are unique and create fixed-sized, irreversible fingerprints of data.

In general, a cryptographic hash function is considered secure if—when fed an arbitrary lump of data—it comebacks a truly random value for every different query while a repeated query comebacks the same random value each time. For example, you could feed “Security is significant” into the hash function, and the output could be “Ten.” But if you feed “Security is a must,” you would get back, say, “13.” Then if you come back to “Security is significant,” the hash function must once again come back “Ten.”

Today’s secure hash algorithms are considered among experts to be SHA-2 and SHA-3.

However, there are certain caveats that a hash function needs to fulfill in order to be perceived secure.

Pre-image resistance

Very first, the hash function must have “pre-image resistance.” In principle, it must not be possible to go rearwards from an response to the related question. This means that if the hash function output is “Ten,” to derive that the original feed was “Security is significant” must not be possible.

2nd pre-image resistance

2nd, the hash function needs to have “2nd pre-image resistance.” In principle, it should not be possible from looking at the feed “Security is significant” to determine that the feed ” Security is always significant” will produce the same result. This means that if a hash function is fed with “Security is always significant” it should not come back “Ten”—which is the result from the feed “Security is significant.”

Collision resistance

Third, the hash function must have collision resistance. This is similar to 2nd pre-image resistance. It must not be possible to lightly find two messages that have the same hashed result. Even however collisions will exist simply due to the limited range of outputs versus the infinite range of inputs, it must not be possible to lightly derive colliding messages.

If both “Security is significant” and “Security is always significant” resolve to “Ten,” it must not be possible to conclude this result independently. It must take a tremendously (read, improbable) amount of brute force work to detect that the two inputs resolve to the same hashed output.

Ericsson Data Centric Security and quantum computing

So where does this leave the Ericsson Data Centric Security solution?

This solution is based on a blockchain architecture, with two fundamental and crucial parts that make it immune to quantum computing threats: the cryptographic algorithm (SHA2-256) and the Merkle signature scheme also known as a binary hash tree.

The Merkle signature scheme

The Merkle signature scheme is based on a mathematical hierarchical hash tree structure, which has the form of a tree in which the lower branches have the largest amount of leaves. The higher in the tree, the smaller the branches and the fewer leaves on each branch, until the top is reached.

Native hash data is fed into the bottom of the tree, as source input. The architectural functionality is then to concatenate all hash data from the lower layers into next layer of branches until the top of the tree is reached, where a root hash is produced. The Merkle tree works in a dedicated and limited time slot of one 2nd. This means that each Merkle tree schema, with the associated concatenation act and the creation of a tree-top root hash, is carried out and processed every 2nd.

Thereafter the Merkle tree schema is ripped down, for the next 2nd to become rebuilt again and again—and so on for every 2nd into the future. What is significant to acknowledge in context to the Merkle tree is that the hash tree is secure as long as it uses a secure hash function in its architecture that is resistant to pre-images and 2nd pre-images as, for example, SHA2-256 or above.

Unique blockchain security

Another fact that makes the Ericsson Data Centric Security blockchain solution unique is the way the blockchain is securely created and processed every 2nd. Since each root hash is time-stamped and cryptographically interlinked with the previous time slot hash value, the digital blockchain ledger becomes immutable for rearwards switches in time, as well as for other general tampering.

So ultimately, against a quantum computing threat, a “ideal” hash function of output size “n” bits still offers strong resistance as long it is above one hundred twenty eight bits—which means two to the (n/Two) power or above, for example, with SHA2-256, a 256-bit output. In this case, the best quantum computer would still need two to the 128th power of simultaneous operations (that is, with current technology too many to be feasible by a enormous margin) to break pre-image resistance. Further a possible attack has to be executed within one 2nd for each leave in the binary hash tree at the same time in order to become close knowing what information is being hashed, concatenated and processed, which is today know to be unlikely even for furture quantum computers, which makes Ericsson Data Centric Security the ideal choice for thwarting quantum computing threats.

Mads Becker Jorgensen

Mads works as Technical Product Manager in the security product line at Ericsson in Kista, Stockholm. Mads has a background as an Information Security Specialist with over fifteen years field work, both forearms on and conceptual, as a consultant via various international security companies. Mads’ concentrate is on the value of holistic security and especially how to prove and measure it by working with people processes and technology. Mads’ beloved question is “If you can´t measure the value of security, how do you truly know it works, and how do you prove it when you need it?” The response is . you simply don´t.

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