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Register a new userThe coin that never sleeps. The privacy preserving usage of Bitcoin in a longitudinal analysis as a speculative asset
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Added by
Panagiotis Papadopoulos
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Bitcoin is the first and undoubtedly most successful cryptocurrecny to date with a market capitalization of more than 100 billion dollars. Today, Bitcoin has more than 100,000 supporting merchants and more than 3 million active users. Besides the trust it enjoys among people, Bitcoin lacks of a basic feature a substitute currency must have: stability of value. Hence, although the use of Bitcoin as a mean of payment is relative low, yet the wild ups and downs of its value lure investors to use it as useful asset to yield a trading profit. In this study, we explore this exact nature of Bitcoin aiming to shed light in the newly emerged and rapid growing marketplace of cryptocurencies and compare the investmet landscape and patterns with the most popular traditional stock market of Dow Jones. Our results show that most of Bitcoin addresses are used in the correct fashion to preserve security and privacy of the transactions and that the 24/7 open market of Bitcoin is not affected by any political incidents of the offline world, in contrary with the traditional stock markets. Also, it seems that there are specific longitudes that lead the cryptocurrency in terms of bulk of transactions, but there is not the same correlation with the volume of the coins being transferred.
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Bitcoin and many other similar Cryptocurrencies have been in existence for over a decade, prominently focusing on decentralized, pseudo-anonymous ledger-based transactions. Many protocol improvements and changes have resulted in new variants of Cryptocurrencies that are known for their peculiar characteristics. For instance, Storjcoin is a Proof-of-Storage-based Cryptocurrency that incentivizes its peers based on the amount of storage owned by them. Cryptocurrencies like Monero strive for user privacy by using privacy-centric cryptographic algorithms. While Cryptocurrencies strive to maintain peer transparency by making the transactions and the entire ledger public, user privacy is compromised at times. Monero and many other privacy-centric Cryptocurrencies have significantly improved from the original Bitcoin protocol after several problems were found in the protocol. Most of these deficiencies were related to the privacy of users. Even though Bitcoin claims to have pseudo-anonymous user identities, many attacks have managed to successfully de-anonymize users. In this paper, we present some well-known attacks and analysis techniques that have compromised the privacy of Bitcoin and many other similar Cryptocurrencies. We also analyze and study different privacy-preserving algorithms and the problems these algorithms manage to solve. Lastly, we touch upon the ethics, impact, legality, and acceptance of imposing these privacy algorithms.
Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoins Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? We present in this paper the first systematic investigation of the degree of decentralization for Bitcoin based on its entire transaction history. We proposed both static and dynamic analysis of Bitcoin transaction network with quantifiable decentralization measures developed based on network analysis and market efficiency study. Case studies are also conducted to demonstrate the effectiveness of our proposed metrics.
In this paper, we attempt to provide a privacy-preserving and efficient solution for the similar patient search problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we let each hospital encrypt its dataset with its own key and outsource the storage of its dataset to a public cloud. The physician can get authorization from multiple hospitals and send a query to the cloud, which efficiently performs the search across authorized hospitals using a privacy-preserving index structure. We propose a hierarchical index structure to index each hospitals dataset with low memory requirements. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the efficiency of the proposed scheme. Notably, we show that under our experimental settings, the proposed scheme is more than $60$ times faster than Wang et al.s protocol and $95$ times faster than Asharov et al.s solution.
Recently, the membership inference attack poses a serious threat to the privacy of confidential training data of machine learning models. This paper proposes a novel adversarial example based privacy-preserving technique (AEPPT), which adds the crafted adversarial perturbations to the prediction of the target model to mislead the adversarys membership inference model. The added adversarial perturbations do not affect the accuracy of target model, but can prevent the adversary from inferring whether a specific data is in the training set of the target model. Since AEPPT only modifies the original output of the target model, the proposed method is general and does not require modifying or retraining the target model. Experimental results show that the proposed method can reduce the inference accuracy and precision of the membership inference model to 50%, which is close to a random guess. Further, for those adaptive attacks where the adversary knows the defense mechanism, the proposed AEPPT is also demonstrated to be effective. Compared with the state-of-the-art defense methods, the proposed defense can significantly degrade the accuracy and precision of membership inference attacks to 50% (i.e., the same as a random guess) while the performance and utility of the target model will not be affected.
Logistics Information System (LIS) is an interactive system that provides information for logistics managers to monitor and track logistics business. In recent years, with the rise of online shopping, LIS is becoming increasingly important. However, since the lack of effective protection of personal information, privacy protection issue has become the most problem concerned by users. Some data breach events in LIS released users personal information, including address, phone number, transaction details, etc. In this paper, to protect users privacy in LIS, a privacy-preserving LIS with traceability (PPLIST) is proposed by combining multi-signature with pseudonym. In our PPLIST scheme, to protect privacy, each user can generate and use different pseudonyms in different logistics services. The processing of one logistics is recorded and unforgeable. Additionally, if the logistics information is abnormal, a trace party can de-anonymize users, and find their real identities. Therefore, our PPLIST efficiently balances the relationship between privacy and traceability.
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