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At the heart of Blockchains is the trustless leader election mechanism for achieving consensus among pseudoanonymous peers, without the need of oversight from any third party or authority whatsoever. So far, two main mechanisms are being discussed: proof-of-work (PoW) and proof-of-stake (PoS). PoW relies on demonstration of computational power, and comes with the markup of huge energy wastage in return of the stake in cyrpto-currency. PoS tries to address this by relying on owned stake (i.e., amount of crypto-currency) in the system. In both cases, Blockchains are limited to systems with financial basis. This forces non-crypto-currency Blockchain applications to resort to "permissioned" setting only, effectively centralizing the system. However, non-crypto-currency permisionless blockhains could enable secure and self-governed peer-to-peer structures for numerous emerging application domains, such as education and health, where some trust exists among peers. This creates a new possibility for valuing trust among peers and capitalizing it as the basis (stake) for reaching consensus. In this paper we show that there is a viable way for permisionless non-financial Blockhains to operate in completely decentralized environments and achieve leader election through proof-of-trust (PoT). In our PoT construction, peer trust is extracted from a trust network that emerges in a decentralized manner and is used as a waiver for the effort to be spent for PoW, thus dramatically reducing total energy expenditure of the system. Furthermore, our PoT construction is resilient to the risk of small cartels monopolizing the network (as it happens with the mining-pool phenomena in PoW) and is not vulnerable to sybils. We evluate security guarantees, and perform experimental evaluation of our construction, demonstrating up to 10-fold energy savings compared to PoW without trading off any of the decentralization characteristics, with further guarantees against risks of monopolization. Index Terms-Blockchain, Proof of Work, Bitcoin, Distributed Ledger, PoW is expensive, Proof of Trust, PoW alternative 1 We refer here to public Blockchains where identities are not managed. Although such systems are technically anonymous, they practically can only guarantee pseudo-anonymity depending on usage patterns and peers practices. 2 We differentiate between Blockchain, with big B, as the technology that comprises all the components of the system in terms of rules, mechanisms, protocols, etc., and between blockchain which refers to the ledger of chained blocks of transactions. 3 e.g., Ethereum Blockchain

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Identity validation of Online Social Networks’ (OSNs’) peers is a critical concern to the insurance of safe and secure online socializing environments. Starting from the vision of empowering users to determine the validity of OSN identities, we suggest a framework to estimate the trustworthiness of online social profiles based only on the information they contain. Our framework is based on learning identity correlations between profile attributes in an OSN community and on collecting ratings from OSN community members to evaluate the trustworthiness of target profiles. Our system guarantees utility, user anonymity, impartiality in rating, and operability within the dynamics and continuous evolution of OSNs. In this article, we detail the system design, and we prove its correctness against these claimed quality properties. Moreover, we test its effectiveness, feasibility, and efficiency through experimentation on real-world datasets from Facebook and Google+, in addition to using the Adults UCI dataset.

Online Social Networks (OSNs) have successfully changed the way people interact. Online interactions among people span geographical boundaries and interweave with different human-life activities. However, current OSNs identification schemes lack guarantees on quantifying the trustworthiness of online identities of users joining them. Therefore, driven from the need to empower users with an identity validation scheme, we introduce a novel model, Cooperative and Adaptive Decentralized Identity Validation CADIVa, that allows OSN users to assign trust levels to whomever they interact with. CADIVa exploits association rule mining approach to extract the identity correlations among profile attributes in every individual community in a social network. CADIVa is a fully decentralized and adaptive model that exploits fully decentralized learning and cooperative approaches not only to preserve users privacy, but also to increase the system reliability and to make it resilient to mono-failure. CADIVa follows the ensemble learning paradigm to preserve users privacy and employs gossip protocols to achieve efficient and low-overhead communication. We provide two different implementation scenarios of CADIVa. Results confirm CADIVa's ability to provide finegrained community-aware identity validation with average improvement up to 36% and 50% compared to the semi-centralized or global approaches, respectively.

At present, high-dimensional data sets are becoming more and more frequent. The problem of feature selection has already become widespread, owing to the curse of dimensionality. Unfortunately, feature selection is largely based on ground truth and domain expertise. It is possible that ground truth and/or domain expertise will be unavailable, therefore there is a growing need for unsupervised feature selection in multiple fields, such as marketing and proteomics. Now, unlike in past time, it is possible for biologists to measure the amount of protein in a cancer cell. No wonder the data is high-dimensional, the human body is composed of thousands and thousands of proteins. Intuitively, only a handful of proteins cause the onset of the disease. It might be desirable to cluster the cancer sufferers, but at the same time we want to find the proteins that produce good partitions. We hereby propose a methodology designed to find the features able to maximize the clustering performance. After we divided the proteins into different groups, we clustered the patients. Next, we evaluated the clustering performance. We developed a couple of pipelines. Whilst the first focuses its attention on the data provided by the laboratory, the second takes advantage both of the external data on protein complexes and of the internal data. We set the threshold of clustering performance thanks to the biologists at Karolinska Institutet who contributed to the project. In the thesis we show how to make a good selection of features without domain expertise in case of breast cancer data. This experiment illustrates how we can reach a clustering performance up to eight times better than the baseline with the aid of feature selection.

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