Privacy-enhancing data aggregation and data analytics in wireless networks for a large class of distributed queries

Abstract Privacy-enhancing techniques and protocols for data aggregation analytics in wireless networks require the development of novel methods efficient privacy-preserving computation distributed queries with protection outcomes from active attackers. Previous approaches to secure incur significant communication overhead cannot be applied big data. This paper proposes two solutions problem outsider attackers a new large class statistical or numerical queries. contains many useful statistics is larger than other classes considered literature previously. We propose Protection Active Attackers (PAA) case computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) homomorphic encryption (PAA-HE). The PAA-HE protocol combines use ElGamal Paillier schemes one system. Theoretical analysis experimental results show that both outperform alternative approaches. provides stronger more PAA-SSS.