While finding out dominant objects over multiple competing parties’ sensitive datasets via multi-party skyline queries (MSQs), upholding ample data privacy with integrity, data and data owners’ (i.e., parties) anonymity, verifiable deeds of entities, the robustness of framework, etc., is crucial. The existing works consider these criteria trivially or partially and may sacrifice data integrity due to malleable cipher data. This article proposes a framework for MSQs that incorporates these requirements together and eventually makes a ranking of parties’ data-objects. To enrich data privacy, it allocates separate decryption keys among mutual entities. To confirm data integrity, it unites encrypted unique tags (UTs) to parties’ encrypted datasets individually. To assure data and parties’ anonymity, it re-encrypts and shuffles encrypted datasets, and uses anonymous credentials, respectively. These qualities and publicly exposed data reinforce the framework and assure the correct deeds of entities. Finally, evaluation results, analyses, comparisons with state-of-the-art works, etc., prove the efficacy of the proposed framework.

This paper proposes a secure offline electronic (e-) payment scheme by adopting Schnorr's untraceable blind signature (BS). Thereby, to satisfy the essential security requirements of e-payment systems, it requires much more simple computations and becomes more practical than many existing schemes. Other considerations are: to prevent the forgery of e-coin, the Bank is only the lawful entity to produce the valid e-coin; and others can verify its correctness. To confirm no swindling, the e-coin owner also sticks her private signing key with the e-coin before spending it as the payment. Hence, through the commitment with challenge-response of Schnorr's BS, the merchant can verify the spent e-coin, and the trusted authority can identify the dishonest spender if multiple spending occurs. Moreover, it embeds three distinct information of date, namely expiration, deposit, and transaction dates with every e-coin. Thereby, it minimizes the size of the Bank's database, correctly calculates the interest of the e-coin, and helps in arbiter if multiple spending, respectively. Finally, it evaluates the performance and analyzes essential security requirements of the proposed scheme, plus studies a comparison with existing ones.

This paper improves the performance of the revised-simplified verifiable re-encryption mixnet (R-SVRM) based e-voting scheme by introducing confirmation numbers (CNs) that are used in the CN based e-voting scheme. Although CN based and R-SVRM based schemes had made e-voting schemes more practical by excluding zero knowledge proof (ZKP) that requires large volume of computations, still they are not efficient enough. Namely, the CN based scheme adopts RSA encryption functions that are not probabilistic or commutative, therefore to satisfy essential requirements of elections, extra random factors are necessary for individual votes, election authorities must sign on votes and they must keep encryption keys as their secrets. On the other hand, although the R-SVRM based scheme uses ElGamal encryption functions that are probabilistic and commutative, vote forms in it is complicated, i.e., vote forms consist of at least 3 items and they include information about candidates as exponents. The improved scheme simplifies vote forms by exploiting CNs, and extensively reduces the number of operations required for individual votes. Also, the scheme successfully satisfies all essential requirements of e-voting systems, i.e., it is endowed with features about privacy, robustness, accuracy, incoercibility and fairness as CN based and R-SVRM based schemes are.

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive–pruning strategy, and different training samples for individual NN’s learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble; (2) maintaining accuracy and diversity of NNs at the same time; and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.

In most of the business areas, many organizations are running similar trades and maintaining comparable databases. These organizations have noticed the importance of analyzing results obtained from the union of databases owned by different organizations. However, they do not want to disclose their contents to others since some of the contents are sensitive and private. Recently, preference-based queries have drawn massive attention in the database community. Particularly, the top-k dominating queries have been studied extensively, which selects the k objects that are better than other objects based on the `domination score'. In this paper, we have considered the top-k dominating queries on the combined databases of different organizations. We propose a secure framework for multi-party top-k dominating queries, where individual organizations do not need to expose their private databases to others. We analyze the privacy of our proposed framework and also evaluate its performance for various settings.

This paper proposes a new electronic voting (e-voting) scheme that fulfills all the security requirements of e-voting. The key mechanism is the one that uses confirmation numbers involved in individual votes to make votes verifiable while disabling all entities including voters themselves to know the linkages between voters and their votes. Unlike complicated zero knowledge proof involved in many e-voting schemes, the confirmation numbers attain the verifiability requirement in a much more simple and intuitive way, then the scheme becomes scalable and practical.