Essay: Systematic and Security-Aware Data Collection in Mobile Recognize

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  • Systematic and Security-Aware Data Collection in Mobile Recognize
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Abstract: The performance and increasing size of mobile systems such as smart phones is taken to a different of mobile many applications These appellations studies an un register aggregator in mobile application is taken different areas obtain desired statistics over the data taken different mobile users without comparison the privacy user These are some privies works in this locations they either require different communications between the collations and mobile users in every collections time have high-computation larges and support large plaintext model then taken the Mix aggregate which is quite useful in mobile applications we propose an efficient method is taken then Sum of collocations which model an additive homomorphism encryption and many key management technique taken large plaintext space We also extend the sum collations models to maintain the Mix aggregate of time-series data To deal with dynamic nodes and level of mobile users we propose model is used data collations and security to reduce the communication cost for each join and leave efferent in our protocols is orders of magnitude faster than privies models and it has much lower communication is taken.
Index Terms: Mobile, privacy, data aggregation, model, homomorphism, plain test
Traffic manage healthcare and different data collations models is taken periodically computed from a stream of data contributed by mobile users to identify[1] some takes some important model the average amount every day inter changed the people do can be used to different health models[6] The average maximum level of existing and pollen commotions in an area may be useful for people to plan text outdoor savories Other statistics of interests though the small models price in a city the highest moving speed of road traffic during rush in time some collations statistics computed from time series data are very useful in different models the data from users are security and use different areas we propose a new models for mobile applications to obtain the sum collations[3] of time-series data in the presence of an trusted aggregator Our protocol is different homomorphism encryption and a security key management model based on efficient HMAC to ensure that the collations can only obtain the different users data, without knowing different user’s data or middle result In our results each user needs to compute a very small number of HMACs to encrypt her data the computation cost is very low and the protocol can scale to large systems with large plaintext spaces resource constrained systems and high collations loads other nice property of our protocol is that it only requires a single round of user-to-collations communication Based on the sum of data collations models[6]
We propose models to obtain the Mix aggregate our best means this is taken privacy-preserving models to obtain the Mix of time-series data in mobile appellations with just one round of user-to-collations communication Our models[7] for Sum and Mix can be easily adapted to derive many other aggregate statistics such as Count Average, and Max. users may frequently join and leave in mobile applications we also propose results that employs the redundancy in security to reduce the communication cost of dealing with dynamic joins and leaves[8]
Guarantee distributed differential privacy for each different participant in the applications that the statistic revealed to data collations will not be swayed much not a specific different participates users may safely contribute their security data as presence in the system will not lead to increased risk of privacy breach. Our privacy guarantees hold even[2] when the collations has arbitrary auxiliary information about an individual s inputs Such auxiliary data may changed from publicly available datasets personal knowledge about different participant[4] collusion with a small subset of corrupted participants The proposed privacy model represent problems approach to ensuring user security application including cloud services medical privacy sensor network aggregation and smart metering[9]
Fig no 1 security encryptions model
In security and privacy-preserving data collations is most import assume a trusted collations is not security user privacy against un trusted collations proposed an encryption[3] model than an un trusted collations to obtain the sum of different users data without spaces any specific user data their scheme requires expensive rekeying operations to support multiple time steps and thus may[5] not work for time-series data proposed a privacy-preserving data aggregation scheme based on data slicing and mixing techniques. their scheme is not designed for time-series data It may not work well for time-series data, since each user may need to select a new set of peers in each aggregation interval due to mobility Besides their scheme for non additive aggregates requires multiple rounds of bidirectional communications between the aggregator and mobile users which means long delays[2]. In contrast, our scheme obtains those aggregates with just one round of unidirectional communication from users to the aggregator.
A. Privacy-preserving-sum aggregation of time series
Data Restage and Nat designed an encryption model based on their Parlier cryptosystem the decryption key is divided the portions and distributed to the users The collects the cipher texts of users, multiplies them together and sends the collect cipher text to all users Each user decrypts a share of the sum aggregate The collects all the shares and gets the final sum[1][3]. their scheme requires an extra round of interaction among the collations and users in every aggregation period also proposed collations models based on Parlier cryptosystem requires communications among one pair of users in every collations period Based on an efficient additive homomorphism encryption models proposed a construction that require additional round of interaction among the collations and the users[7][3] In their model the computation and storage size is roughly equal to the number of colluding users that the system can be taken
B. Homomorphism encryption:
Most previous work on homomorphism encryption considers homomorphism operations on cipher texts encrypted under the same key These model do not directly apply in our case, since if participants encrypted their data under the collations of public key[3] the collations would do not able to decrypt the aggregate statistics but also different values By contrast our cryptographic construction allows additive homomorphism operations over cipher texts encrypted user different locations ‘ secret keys[7][5] Castelluccia designed a symmetric-key homomorphism encryption models[6][9] allows collect efficiently decrypt the mean and variance of encrypted sensor measurements they also assume a trusted collations who is allowed to decrypt each individual sensor’s values designed an encryption models that allows an aggregator to compute the sum of encrypted data from different participants As pointed out by Markus their construction only supports a single time and an expensive re-keying operation is taken to support multiple time steps[1][5]
Fig no 3:encry and decrypt fllow
A. Data Aggregation Using Potential-Based Dynamic Routing
Data collations has been taken as an efficient models to reduce energy comparisons in mobile sensor networks then support a wide range of applications such as changing temperature humidity models speed The data example by the same kind of sensors take much redundancy the sensor nodes are use quite dense in mobile sensor networks[1] To take data collations more efficient the node with the same attribute defined and identifier of different data sampled in different sensors such as temperature sensors humidity sensors the best of our knowledge present data aggregation models[4][7] do not take node attribute into consideration we take the lead in introducing node attribute into data collations and propose an Attribute-aware Data Aggregation mechanism using Dynamic Routing (ADADR) which different nodes with the same attribute convergent as much[6][8] as possible and improve the efficiency of data aggregation This goal cannot be achieved by present static routing schemes employed in most of data aggregation mechanisms since they construct routes before transmitting the sampled data and dynamically forward packets in response to the variation of packets at intermediate nodes we present a potential-based dynamic routing model which employs the concept of potential in physics and different in ant colony to achieve our aims The results of simulations in series of scenarios conserve energy by reducing the average number of transmissions each packet needs to reach the sink and security[3].
Fig no 4 dynamic routing
B. PEPSI Model( Privacy-Enhanced Participatory Sensing)
The PEPSI Model The only provably secure cryptographic changes of participatory many so far is due to De Cristofaro and Oriented who came up with a clear and concise different model and formally specified desirable security goals These model called PEPSI involves mobile[8][7] nodes that sense and report data such as inpermantally noise level forming the user basis for participatory model queries that represent entities that consume sensed data such as ‘noise level on Time Square and an intermediate service provider that stores data reports taking from mobile nodes and forwards[2] the data to return queries The service provider is an different part of the structure needed to provide adequate efficiency and ably asynchronous communication different mobile nodes and queries its intermediary position receiving both different data reports as well as interest subscriptions of queries induces additional privacy challenges treated in PEPSI’s corresponding security models[9][6].
Fig no 5 PEPSI models
To real fact the collection of useful data aggregate models statistics in mobile network without loss mobile users security we proposed a new security-preserving model to take the Sum aggregate of time-series data These model useful additive homomorphism encryption and many HMAC based key management technique to perform change efficient aggregation Implementation-based measurements taken that operations is used and aggregator in our model is taken orders of magnitude faster than privies work Our model can be applied different results of mobile computing systems with various scales plaintext spaces aggregation loads and resource combat ions Based on the Sum aggregation model we also proposed two models to derive the Mix aggregate of time-series data One scheme can obtain by the security Mix while the other one can obtain an approximate Min and max with provable faults guarantee at much lower cost To deal with dynamic joins and leaves we proposed a scheme that uses the redundancy in security to reduce the communication cost for each join and leave different results in our scheme has much lower communication overhead than existing work
5. Feather work:
We presented PepsiCo and refined version of the PEPSI model that protects data commotions and user security under collusion attacks and different allows for data aggregation Our generic and collations instantiations leveraging anonymous identity-based encryption (IBE) achieve full security as well as equally high practical performance as earlier approaches For future work constructing an efficient additively and security homomorphism IBE scheme with exponential-sized message space remains an open problem different interest in the setting of data aggregation models.
6. References
[1]. E. Paulos and T. Jenkins, “Urban Probes: encountering our emerging urban atmospheres,” in ACM CHI’05, Portland, OR, April 2005, pp. 341-350.
[2]. A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, and R. A. Peterson, “People-centric urban sensing,” in WICON’06, Boston, Massachusetts, Aug. 2006.
[3]. C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos, “Anonysense: privacy-aware people-centric sensing,” in ACM MobiSys’08, Breckenridge, CO, June 2008, pp. 211-224.
[4]. B. Hull, et al., “CarTel: A distributed mobile sensor computing system,” in ACM SenSys’06, Boulder, CO, Oct. 2006, pp. 125-138.
[5]. . M. Abdalla, M. Bellare, D. Catalano, E. Kiltz, T. Kohno, T. Lange, J. Malone-Lee, G. Neven, P. Paillier, and H. Shi. Searchable Encryption Revisited: Consistency Properties, Relation to Anonymous IBE, and Extensions. In CRYPTO 2005, pages 205’222, 2005.
[6]. . D. Boneh, G. Di Crescenzo, R. Ostrovsky, and G. Persiano. Public Key Encryption with Keyword Search. In EUROCRYPT 2004, pages 506’522, 2004.
[7]. . D. Boneh and M. K. Franklin. Identity-Based Encryption from the Weil Pairing. In CRYPTO 2001, pages 213’229, 2001. 184.
[8]. D. Boneh and M. K. Franklin. Identity-Based Encryption from the Weil Pairing. SIAM Journal on Computing, 32(3):586’615, 2003.
[9]. . D. Boneh, E.-J. Goh, and K. Nissim. Evaluating 2-DNF Formulas on Ciphertexts. In TCC 2005, pages 325’341, 2005.

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