Binance Responds To Liquidity Transparency

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Binance
Binance

Binance, the world’s largest cryptocurrency exchange by trading volume, has published its first official proof-of-reserves audit. The move was aimed at increasing transparency and reassuring customers that their funds are safe. In a blog post published recently, Binance explained that it used a Merkle Tree method to verify its fiat and crypto assets.

In a press release published on the company’s official website, Binance published an official proof-of-reserves report containing data from the year-end balances of its fiat and crypto assets.

Binance has published an official proof-of-reserves audit on its blog as part of a new initiative to increase transparency.

Binance Make Their Move

The report contains data from the year-end balances of Binance’s fiat and crypto assets. The exchange claims that it conducted an internal audit during the month of December, which included a review of its consolidated financial statements and blockchain transactions, as well as other procedures such as manual checkouts and cross references with internal databases. The results were then verified by an independent third party accounting firm called KPMG.

According to the announcement published on Binance’s blog on Jan 1., the following assets have been used for this audit: 100% USD held in reserve; 100% BTC held in cold wallets; 99% ETH held offline (cold wallet); 99% EOS locked up in smart contract; 99% NEO held offline (cold wallet).

The blog post notes that the verification method chosen by Binance is based on a Merkle Tree, a data structure used to validate the integrity of stored data. A Merkle Tree is an acyclic binary tree for storing and verifying information, which can be applied to any type of data (e.g., SHA256 hashes).

It can also be used to verify whether or not you have all the bits needed for your transaction, without having to download them from another party or node. This saves bandwidth and processing power—and it’s especially important when you’re dealing with large datasets like those found in blockchain technology.